Q1 2024 Recursion Pharmaceuticals Inc Earnings Call

May 9, 2024

One of them run through some of the exciting updates from the company over the last quarter as we move forward to achieve our mission of Dakota biology to radically improve lives.

And so with that of course, some disclaimers and recursion I think is uniquely positioned to hit the tech bio escape velocity and what I mean by that is I think we have pretty uniquely put together the pipeline the platform and the people that are giving us the opportunity over the coming quarters and years to reach.

Start to demonstrate a shift in the pace the scale and hopefully also the probability of success of drug discovery and development and we're just delighted to be in this position to be helping to lead tech bio because we feel so confident about what the future looks like we feel that the future of tech bio is the inevitable future of biotech and weird.

Delighted to be leading that and so thankful that all of you are supporting us.

So with that I'm going to go ahead and dive into a little bit of what we're working on and I'm going to start with the pipeline and the pipeline in particular I think is very exciting because we have an opportunity to start to demonstrate catalysts on a roughly quarterly cadence. These catalysts are going to start in Q3 with our first phase.

You read out and then again, we're going to start to be able to demonstrate phase two POC readouts on a quarterly cadence will kick off with Rec 99 for the Sycamore trial in cerebral Calvinists malformation. This is our first in disease opportunity, where we're really leading out as the first institutional sponsored program that's gone through the.

The FDA, we are nearly complete not only with the study, but enrolling almost all of the patients into a long term extension and what we'll be looking for in the context of this trial is looking across all the evidence from all of these exploratory endpoints not only its safety and Tolerability, where we believe we've got a really strong.

Wrong opportunity, but certainly at a variety of different potential efficacy readouts that we could work with the FDA to move forward with to try and get this medicine to patients as quickly as we can we'll follow that up with another program to two way too in neurofibromatosis type two where we've given guidance that we think will be will be reading out the preliminary safety and efficacy in Q4.

We've got that wrecked for 881 with a preliminary safety and efficacy readout in the first half of 2025, we've got another rack for a one program again with another preliminary Ah.

Safety and efficacy readout in the first half of 2025, and then a number of other programs coming behind that either initiating phase II studies or moving into IND submission in the near term. So really excited about this pipeline I think it gives <unk> a unique opportunity to start to demonstrate in a really robust way.

Hey shots on goal and of course, we know all of these programs won't be successful, but we believe some may be and ultimately what's exciting to us is not only the opportunity to bring medicines to patients in many cases first in disease, but the opportunity to begin to learn the opportunity to begin to take the data from these trials feed it back into.

The platform and whether it's a success or a failure to be able to improve the platform. Because we really are in a multi decade journey to build what we think will be the tech bio company that defines the future and it's not only our pipeline that we're excited about it's our platform and we're delighted to share with today's earning.

A lot like the deal we did with Tempus back in December a deal here with helix helix is a fantastic company, we've been getting to know them for awhile, they've partnered with health care systems, all across the country to bring really significant scale of exome genomics and longitudinal health data into a robust source.

We're environment, where like we did with the 10th this data we've now signed a collaboration with helix, that's going to give us access to hundreds of thousands of D identified records along with <unk> data that we can put together with all of the rest of the data we've generated at recursion and start to continue to train these causal AI models.

To help understand the gene networks that are underlying now not only oncology diseases, but also non oncology diseases and what's fantastic about this collaboration is that as you know we've got large partnerships in the context of Roche Genentech in neuroscience, and maybe other partnerships coming down the line and non oncology areas and then.

This collaboration will allow us to be generating even more robust hypotheses using using patient data to help drive our really really exciting platform internally. So very excited to announce this collaboration with the helix team multiyear collaboration here and we're going to be kicking that off imminently.

So that we could work with the FDA to move forward with to try and get this medicine to patients as quickly as we can we'll follow that up with another program rack to two way too in neurofibromatosis type two where we've given guidance that we think will be will be reading out the preliminary safety and efficacy in Q4. We've got then rack for 881 with a preliminary Ah.

Second we shared this on social a couple of weeks ago, but just want to emphasize you know recurs and pioneered the use of pheno mix to try and understand biology, but we now also have started to scale multiple other layers of Omega transcriptome. It's working on proteomics, we have in vivo them, because we have the patient data like I just talked about.

Safety and efficacy readout in the first half of 2025, we've got another rack for a one program again with another preliminary a safety.

But I really want to emphasize here are working transcript domains. We've now sequenced our one millionth transcriptome ever kirschman and we are leveraging our unique take on doing transcript homes in order to work across a whole genome transcriptome ex map the first of potentially many in that space and also starting to layer in.

Shots on goal and of course, we know all of these programs won't be successful, but we believe some may be and ultimately what's exciting to us is not only the opportunity to bring medicines to patients in many cases first in disease, but the opportunity to begin to learn the opportunity to begin to take the data from these trials feed it back into the <unk>.

Data from Transcriptome X across Ah.

Chemical perturbations as well we think this will be a really exciting orthogonal data layer to what we're building with Pheno mix, what we're going to build with proteomics, what we're building with in vivo mix in the in the animal organism step and what we've got with genome scale population scale data not only with tempus, but now with with helix and so not only are we training.

Platform and whether it's a success or a failure to be able to improve the platform. Because we really are in a multi decade journey to build what we think will be the tech bio company that defines the future and it's not only our pipeline that we're excited about it's our platform and we're delighted to share with today's earnings.

Our AI models within each of these data layers, but increasingly starting to train AI models between and across these different data layers. These multi modal omits datasets that we're generating I think pretty uniquely here recursion, so really exciting and congratulations to the team for the incredible scale that they've put together with our transcriptome X technology.

Allergy.

Finally, another exciting opportunity on the platform side, we've really been leaning into active learning over the past quarter and I think youre going to see us continue to increase the cadence of this work. This research work coming out of the team at Valence labs and being done here at recursion, which is allowing us to move away from having to do every experiment towards.

Environment, where like we did with the 10th this data we've now signed a collaboration with helix, that's going to give us access to hundreds of thousands of D identified records along with <unk> data that we can put together with all of the rest of the data we've generated at recursion and start to continue to train these causal AI models to.

Making predictions about what experiment would give us the highest amount of learning and what you can see here is one of our early pilots looking across just about 50 or 100 genes where by using this iterative approach of active learning, we can get about 80% of the value 80% of the information you see on that top line only doing about.

Help understand the gene networks that are underlying now not only oncology diseases, but also non oncology diseases and what's fantastic about this collaboration is it as you know we've got large partnerships in the context of Roche Genentech in neuroscience, and maybe other partnerships coming down the line and non oncology areas and this <unk>.

40% of the experiments and we think this is going to be a transformational tool for us to deploy with our unique scaled wet lab environment because what this is going to allow us to do is start to explore this incredible sort of.

Operations will allow us to be generating even more robust hypotheses using using patient data to help drive our really really exciting platform internally. So very excited to announce this collaboration with the helix team multi year collaboration here and we're going to be kicking that off imminently.

Hi dimensional space of biological perturbations chemical perturbations time based perturbations across multiple different layers of <unk> and it starts to become trillions and trillions and trillions in fact, trillions times Trillions times trillions of different possible combinations, one could explore far too much to ever brute force and use.

Second we shared this on social a couple of weeks ago, but just want to emphasize you know recursion pioneered the use of pheno mix to try and understand biology, but we now also have started to scale multiple other layers of Omega transcriptome. It's working on proteomics, we have in vivo them, because we have the patient data like I just talked about.

These kinds of tools and technologies, we are building out of recursion I think we're gonna be uniquely positioned to do the best next experiment to use our resources in the most efficient way to broadly map drug discovery to broadly map biology to broadly map chemistry, and ultimately bring medicines to patients more quickly.

But I really want to emphasize here are working transcriptome X weave now sequenced, our one millionth transcriptome ever kirschman and we are leveraging our unique take on doing transcript homes in order to work across a whole genome transcriptome ex map the first of potentially many in that space and also starting to layer in day.

And then I also wanted to talk a little bit about some exciting news around <unk> two again, we announced a little bit of this on social we partnered with Nvidia last year. They brought an equity investment in and since then we've been working so closely with that team announced in the fall that we were going to build a biohacker two our next supercomputer and you can see here that we have.

<unk> from Transcriptome X across.

Chemical perturbations as well we think this will be a really exciting orthogonal data layer to what we're building with Pheno mix, what we're going to build with proteomics, what we're building with in vivo mix in the in the animal organisms step and what we've got with genome scale population scale data not only with tempus, but now with with helix and so not only are we trade.

Actually now have built out this supercomputer we benchmark the supercomputer at 23.32 Petaflops, we did that once all the materials were in place we built it out and Benchmarked. It in three weeks. Thanks to an incredible partnership with the Nvidia team and if we were to take that benchmark and compare it to the previous top 500 list it would put us right around our <unk>.

<unk>, our AI models within each of these data layers, but increasingly starting to train AI models between and across these different data layers. These multi modal omits datasets that were generating I think pretty uniquely here recursion, so really exciting and congratulations to the team for the incredible scale that they've put together with our transcriptome X.

30th position there so we'll see as the new top 500 list comes out where we end up probably in the top 50, but this means recursion now owns and operates.

The two of the fastest.

Supercomputers in all of Biopharma will be combining <unk> with <unk> two will re benchmark that later and see if we can and continue to move up the path, but I think in the in the space of a.

Technology.

<unk> Tec bio, where we know scaling laws apply where we know both data and computer gonna be critical recursion really uniquely positioned with one of the most compelling datasets one of the fastest growing datasets one of the most purpose built for machine learning datasets alongside one of them.

Most robust sets of on premise compute in the industry and putting those two things together, we think gives us a really really robust mode. So kudos to the team for all of their hard work getting this done.

And now I want to finish by talking just a little bit about the people because ultimately with out of the people. We can't drive. This mission forward. We had a couple of really big announcements. This last quarter. The first was the shock Con I think one of the icons of tech bio moving from J&J to join US here at recursion, She's already joined our board of directors.

And in the next sort of six to 10 weeks she'll be moving over as our Chief R&D Officer, and Chief Commercial officer here at recursion. She brings I think incredible vision from hit and target discovery, all the way through to using digital tools for commercialization marketing and distribution problems in that that vision.

These kinds of tools and technologies, we are building out of a cushion I think we're going to be uniquely positioned to do the best next experiment to use our resources in the most efficient way to broadly map drug discovery to broadly map biology to broadly map chemistry, and ultimately bring medicines to patients more quickly.

Generic arc across every step of drug discovery and development her experience doing pipeline strategy of J&J and I think J&J really one of the leaders in thinking about digital tools in the Biopharma space. We're just delighted to have <unk> join us here at recursion and can't wait for the next stage with her and then we also announced.

Michael Bronstein, the deep mine professor of artificial intelligence at Oxford has a joy has joined us as our recursion valence.

Advisor and we're so excited to join him in London in a few weeks as we formally opened our London office.

You can see here that we actually now have built out this supercomputer we benchmark the supercomputer at 23.32 Petaflops, we did that once all the materials were in place we built it out and Benchmarked. It in three weeks. Thanks to an incredible partnership with the Nvidia team and if we were to take that benchmark and compare it to the previous top 500 list it would put it.

Where we think there's just such a exciting talent arbitrage so much great talent in the computational biology space exists in London, and we aim to consolidate a lot of that great talent as we advance this exciting mission forward for recursion before I move to questions I just want to end with some high level guidance I think as I shared before recursion really unique.

Right around our 30th position there. So we will see as the new top 500 list comes out where we end up probably in the top 50, but this means recursion now owns and operates.

Leading tech bio and we believe that over the coming quarters and coming years, I think recursion has the opportunity with a wide variety of catalysts to really start to demonstrate the potential of our philosophy for for drug discovery on the pipeline side, we've got multiple phase II trial starts in 2024.

The two of the fastest supercomputers.

Supercomputers in all of Biopharma will be combining biohacker, one with Biolife too will re benchmark that later and see if we can continue to move up the path, but I think in the in the space of a.

Multiple phase II readouts over the next 18 months on roughly a quarterly cadence and our hope is that if our pipeline continues to operate will be able to meet or exceed that cadence in the future. We've got multiple <unk> that we think are going to happen in the near term on the partnership side. We continue to move forward with our colleagues at Roche Genentech on a real.

<unk> Tec bio, where we know scaling laws apply where we know both data and computer gonna be critical recurs and really uniquely positioned with one of the most compelling datasets one of the fastest growing datasets one of the most purpose built for machine learning datasets alongside one of the Roomba.

Pioneering visionary collaboration and so excited to be working with both the G Red and P. Red teams, we've got the potential for near term program and map options on top of the program option, we already announced in oncology last fall with our our ongoing collaboration with Bayer, we see significant opportunity in the near term.

And now I want to finish by talking just a little bit about the people because ultimately we are out of the people. We can't drive. This mission forward. We had a couple of really big announcements. This last quarter. The first was the shock Con I think one of the icons of Tac bio moving from J&J to join US here at recursion, She's already joined our board of directors.

In the space of Undruggable oncology targets for some program options.

We've got a fantastic collaboration with Tempus and we've already identified some pretty exciting targets in the context of non small cell lung cancer, and we're starting to integrate larger chunks of their data for broader kind of pan cancer causal AI models that are giving us really exciting hypotheses in oncology and then as we've shared.

We see the opportunity for additional transformational partnerships in the context of non oncology areas areas, perhaps like cardiovascular and metabolism and so we'll be looking forward, we hope to being able to share more details there in the near future and then finally as you saw as we shared our Phenom Beta Foundation model for <unk>.

For commercialization marketing and distribution problems in that that visionary arc across every step of drug discovery and development. Her experience doing pipeline strategy of J&J and I think J&J really one of the leaders in thinking about digital tools in the Biopharma space. We're just delighted to have <unk> join us here.

Image based drug discovery with Nvidia on bio Nemo, we continue to do business experiments with our data using it as a value driver and have the potential to put some of our data some of our tools into a variety of different marketplaces, a variety of different partnerships and we hope to be able to share more there soon and then finally.

Recursion and can't wait for the next stage with her and then we also announced Michael Bronstein. The deep mine Professor of artificial intelligence at Oxford has a joy has joined us as our recursion valence.

Advisor and we're so excited to join him in London in a few weeks as we formally opened our London office.

Our platform, we're moving the recursion O S from automated discovery, which is where I would argue we are today increasingly workflows and processes that are being driven in an automated way towards autonomous discovery and our low platform that we unveiled at J P. Morgan is a stepping stone in this direction towards autonomous discovery.

AI agents will be leveraging the tools that we built it recurs in both wet lab and dry lab to automatically hypothesize about biology automatically look for high areas of unmet need and automatically prioritize experimentation to give us the fastest route to impact for patients we feel like that's where the industry is going to move.

Move and we want to make sure we're leading that and then finally I just wanted to share some of the things that I feel like our on our roadmap and the in the near term active learning Ive talked about proteomics I've talked about but we're also doing a lot of exploration around the potential for org organoid in spheroidal to help us increase both translation and kind of predictive add me Todd.

<unk> phase II Readouts over the next 18 months on roughly a quarterly cadence and our hope is that if our pipeline continues to operate will be able to meet or exceed that cadence in the future. We've got multiple <unk> that we think are going to happen in the near term on the partnership side. We continue to move forward with our colleagues at Roche Genentech on are really.

<unk> at scale it recurs and those are kind of bottlenecks that were working on now and then of course on the automated synthesis side. We continue to think that the time that it takes to synthesize small molecules eight to 12 weeks is we've been bringing that down through our collaboration with enemy, but we see a real opportunity to continue to accelerate that through <unk>.

Pioneering visionary collaboration and so excited to be working with both the G read an P. Red teams, we've got the potential for near term program and map options on top of the program option, we already announced in oncology last fall with our our ongoing collaboration with Bayer, we see significant opportunity in the near term.

Things like automated synthesis and automated micro synthesis and finally, we're ending Q1 with nearly $300 million in cash.

In the space of Undruggable oncology targets for some program options.

At the end of Q1, and I think that gives us a robust runway.

And recursion really feels poised with some of these milestones that I've just mentioned in the near term these potential milestones, giving us pretty significant runway extension as we begin to hit those so we feel like we're in a really fantastic space, we're honored to be helping to lead the tech bio evolution as we move biotech Intertek bye.

And we're so thankful for all of your questions and support and with that I'm going to go ahead and move over to start answering some some questions. So let's dive right in.

It looks like we've got a question from Dante Noah Eric Eric Joseph on the team at J P. M. Gil Blum et cetera, similar question here and Theyre asking what does success look like for each of your upcoming CCM and F. Two F. AP accident when a P. C phase two trial Readouts and what might your next steps be.

And I think just given the time today I won't go through each of these programs individually, but what I will share is for each of these programs. We are likely to be first in disease or near first in disease and when you have an opportunity like that I think we really have to work with our colleagues at the agency work with key opinion leaders work with patient advocates to <unk>.

Look at the some of the evidence and of course, we'll be looking for a therapeutic window, but we will be looking for early signals of efficacy across a variety of different readouts in the context of CCM. For example, we could imagine looking for objective.

Where AI agents will be leveraging the tools that we built it recurs in both wet lab and dry lab to automatically hypothesize about biology automatically look for high areas of unmet need and automatically prioritize experimentation to give us the fastest route to impact for patients we feel like that's where the industry's going to me.

Improvement in things like hemosiderin deposition around lesions as well as subjective improvements in things like patient reported outcomes or other kind of nurture a neurologist a reported outcome tools that we have in in the secondary endpoints and I think as we look at the some of the evidence for each of these will work very closely with key opinion leaders and <unk>.

Move and we wanted to make sure we're leading that and then finally I just wanted to share some of the things that I feel like our on our roadmap and in the near term active learning Ive talked about proteomics I've talked about but we're also doing a lot of exploration around the potential for org organoid in spheroidal to help us increase both translation and kind of predictive add me too.

<unk> and the FDA and what we want to see us moving the biology, if we see that we're moving in the biology in a way that is going to be meaningful for patients potentially that's going to be the signal we want to see to drive forward and have discussions with the agency and the next steps will be to aggressively pursue whatever it is.

We can to move these medicines to patients in some context like N F. Two it might be moving to start our phase III trial and in consultation with the agency in other contexts, we might even have discussions with the agency about the potential for accelerated approval, but we're going to really need to see what the data looks like and we'll be looking forward to reporting that in Q3.

Things like automated synthesis and automated micro synthesis and finally, we're ending Q1 with nearly $300 million in cash.

In Q4 in the first half of 2025 with more programs coming in the future.

At the end of Q1, and I think that gives us a robust runway.

Alright, moving onto another question here, Steve Dechert from Keybanc and Vikram per head for Morgan Stanley are asking how should we think about the significance of your phase II readout for Rec 99, four in terms of validating your platform and the potential for other programs in your pipeline. This is a great question and when we get asked pretty frequently.

And you know recursion really feels poised with some of these milestones that I've just mentioned in the near term these potential milestones, giving us pretty significant runway extension as we begin to hit those so we feel like we're in a really fantastic space, we're honored to be helping to lead the tech bio evolution as we move biotech Intertek bye.

So Steve Vikram, what I can say is you know I think we've already got a lot of leading indicators of the power of this platform as I've shared before you can go back and look at the paper that we published a preprint in April of 2020. We're now we're nine for 10 at predicting the outcome of FDA approved drugs in the context of Sars Cov, two virus and we made all of those.

Speaker Change: And we're so thankful for all of your questions and support and with that I'm going to go ahead and move over to start answering some some questions. So let's dive right in it.

Speaker Change: It looks like we've got a question from Dante Noah Eric Eric Joseph on the team at J P. M. Gil Blum et cetera, similar question here and Theyre asking what does success look like for each of your upcoming CCM and F. Two F. AP accident when a P. C phase two trial Readouts and what might your next steps be.

Predictions well ahead of time, but as we all know there's a lot that goes into every clinical trial a lot of resources and trial design that can really influence the outcome and the probability of success of the average phase two is somewhere between 25 and 35% and so we know that there will we hope be both successes and we know there may be some failures or.

Speaker Change: And I think just given the time today I won't go through each of these programs individually, but what I will share is for each of these programs. We are likely to be first in disease or near first in disease and when you have an opportunity like that I think we really have to work with our colleagues at the agency work with key opinion leaders work with patient advocates to la.

Similarly, I think theres, an uncorrelated opportunity with each of the programs that we have moving forward and by that I mean, technically uncorrelated, because even though we're using the same platform to identify each potential opportunity. We are validating those opportunities against the same gold standard standard animal models pdx.

Speaker Change: Look at the some of the evidence and of course, we'll be looking for a therapeutic window, but we will be looking for early signals of efficacy across a variety of different readouts in the context of CCM. For example, we could imagine looking for objective.

G models et cetera that anyone else in the industry would would use and so well, we certainly think a positive trial.

Gives people a lot of optimism around what we're building if any of these trials are negative then we certainly could imagine that the technical risk is relatively uncorrelated between each of these and that's why we're pushing so hard to advance a whole pipeline of programs with Readouts coming on a quarterly cadence what's more we've got programs that are now moving.

Speaker Change: Improvement in things like hemosiderin deposition around lesions as well as subjective improvements in things like patient reported outcomes or other kind of nook nook.

Speaker Change: Neurology ripped.

Speaker Change: Our reported outcome tools that we have in in the secondary endpoints and I think as we look at the some of the evidence for each of these will work very closely with key opinion leaders and patients and the FDA and what we want to see us moving the biology, if we see that we're moving in the biology in a way that is going to be meaningful for patients.

Forward with pharma partners, we've got the potential for additional programs to move forward and we've got the potential for driving value through our data and through our computational and software opportunity. So I think recursion. Unlike a traditional biopharma company really doesn't have the same bimodal risk that many other companies in this space.

Speaker Change: <unk> potentially that's going to be the signal, we want to see to drive forward and have discussions with the agency and the next steps will be to aggressively pursue whatever it is we can to move these medicines to patients in some context like Nf two it might be moving to start our phase III trial and in consultation with the agency in other con.

Do who will typically advance one or two drugs to this big sort of phase III to readout, great. Thanks for that question.

Next I'm going to go to Mary asked and Gil Whatsapp.

What success have you had to date with using the Tempus data what other population genomics data might you look to access and how could such data complement what you were able to learn from Tempus great question. So we've already leverages the tempus data.

Speaker Change: <unk>, we might even have discussions with the agency about the potential for accelerated approval, but we're going to really need to see what the data looks like and we'll be looking forward to reporting that in Q3 in Q4 in the first half of 2025 with more programs coming in the future.

Signs of that collaboration in either late November early December last year.

Speaker Change: Alright, moving onto another question here, Steve Dechert from Keybanc and Vikram per head for Morgan Stanley are asking how should we think about the significance of your phase II readout for Rec 99, four in terms of validating your platform and the potential for other programs in your pipeline. This is a great question and when we get asked pretty frequently so Steve Vikram, what I can say.

<unk> data coming in within weeks the team worked over the holidays and we had deployed some of our early AI models onto Tempus data in the first really by the start of J P. Morgan in the first couple of weeks of January.

We've continued to refine that work and as I shared earlier, we've already identified an exciting novel opportunity in the context of non small cell lung cancer. We have a program. That's now moving forward using the tempus data and I think were really uniquely positioned to take the tempus data alongside the proprietary we've proprietary.

Speaker Change: As you know I think we've already got a lot of leading indicators of the power of this platform as I've shared before you can go back and look at the paper that we published a preprint in April of 2020. We're now we're nine for 10 at predicting the outcome of FDA approved drugs in the context of Sars Kobe two virus and we made all of those predictions well ahead of time.

Data, we've generated at recursion to bring those together to identify targets that really you wouldn't be able to identify without these complementary datasets and so you'll see us continuing to move program programs for it that way and we hope as those programs hit kind of the preclinical stage, we'll be able to share more about them, but as we just announced today in our collaboration with helix. We're all.

Speaker Change: But as we all know there's a lot that goes into every clinical trial, a lot of resources and trial design that can really influence the outcome and the probability of success of the average phase two is somewhere between 25 and 35% and so we know that there will we hope be both successes and we know there may be some failures ultimately I think theres an.

So now looking at sort of non oncology scaled population scale genomics transcriptome X data and we think that's really fascinating opportunity not only for the same play can we take that data looking across large non oncology diseases, maybe a neuroscience maybe in cardiovascular metabolism.

Speaker Change: Correlated opportunity with each of the programs that we have moving forward and by that I mean, technically uncorrelated, because even though we're using the same platform to identify each potential opportunity. We are validating those opportunities against the same gold standard standard animal models Pdx oncology models et cetera there.

Combine it with our internal data to identify sort of this this combination afford and reverse genetics that can move the company forward probably in some of our partnerships either existing or future partnerships, but also I would say I think there's some opportunity in oncology and non oncology space to actually use both the tempus <unk>.

Speaker Change: Anyone else in the industry would would use and so well, we certainly think a positive trial.

Speaker Change: Gives people a lot of optimism around what we're building if any of these trials are negative then we certainly could imagine that the technical risk is relatively uncorrelated between each of these and that's why we're pushing so hard to advance a whole pipeline of programs with Readouts coming on a quarterly cadence what's more we've got programs that are now moving forward.

And the helix data along with our underlying data to get a sense of how these genetic.

These gene networks really work, knowing how they're perturbed in the context of oncology settings, and how they're perturbed in the context of non oncology settings, I think will give us a really robust a field from which to work robust substrate. You know it takes a while to take a discovery program and get it into the preclinical space, but rest assured maryann Gil as weak.

Speaker Change: With pharma partners, we've got the potential for additional programs to move forward and we've got the potential for driving value through our data and through our computational and software opportunity. So I think recursion. Unlike a traditional biopharma company really doesn't have the same bimodal risk that many other companies in this space.

Those programs into IND, enabling studies, we look forward to being able to share quite a bit more.

Speaker Change: Do who will typically advance one or two drugs to this big sort of phase III to readout, great. Thanks for that question Nick.

Alright next we're going to go to Laura who asks about our London office. What are we what are we looking for in the London Office why are we opening our London office and what our international growth plans beyond that a great question, Laura we like to operate in cities in communities, where we feel like there's an arbitrage where there's great talent.

Speaker Change: Next I'm going to go to Mary asked and Gil what success have you had to date with using the tempus data what other population genomics data might you look to access and how could such data complement what you were able to learn from Tempus great question. So we've already leverages the tempus data.

Speaker Change: <unk> signed that collaboration in either late November early December last year had.

And maybe fewer companies that are leveraging that talent, we're based in Salt Lake City. We've got fantastic teams really focused in software engineering in Toronto and other related areas.

Gil: Had data coming in within weeks the team worked over the holidays and we have deployed some of our early AI models onto Tempus data in the first.

Digital chemistry as well, we've got a great team in AI and AI research and Montreal, We've got a fantastic team in San Jose Milpitas with our in vivo facility in London and felt like an opportunity for us to accelerate our computational biology talent, we think the U K has done a really tremendous job of training.

Mary: Really by the start of J P. Morgan in the first couple of weeks of January.

Speaker Change: We've continued to refine that work and as I shared earlier, we've already identified and exciting novel opportunity in the context of non small cell lung cancer. We have a program. That's now moving forward using the tempus data and I think were really uniquely positioned to take the tempus data alongside the proprietary we've proprietary.

<unk>.

Looks at the intersection of data science, and computation with biology, and chemistry really probably ahead of the universities in the U S. In terms of that integrated training and we add nearly 300 applicants in just the first couple of days, when we announced our London office for just a couple of dozen positions that we posted and these were extraordinarily talented folks.

Speaker Change: Data, we've generated at recursion to bring those together to identify targets that really you wouldn't be able to identify without these complementary data sets and so you'll see us continuing to move program programs for it that way and we hope as those programs hit kind of the preclinical stage, we'll be able to share more about them, but as we just announced today in our collaboration with helix where else.

So we feel like that that is already paying off with fantastic talent in London as far as other international plans. You know I think that office is probably going to be a fantastic step for US internationally. We're still only a team of 530 or 540 folks. So I don't think Youll see us do a lot of additional international.

Speaker Change: So now looking at sort of non oncology scaled population scale genomics transcriptome X data and we think that's really fascinating opportunity not only for the same play can we take that data looking across large non oncology diseases, maybe a neuroscience maybe in cardiovascular metabolism.

<unk> growth in the in the near term, but certainly as the company begins to move into development begins to scale our development ambitions, maybe even thinks about commercialization in the intermediate to long term will have an opportunity to grow in places like Asia.

Speaker Change: Combine it with our internal data to identify sort of this this combination afford and reverse genetics that can move the company forward probably in some of our partnerships either existing or future partnerships, but also I would say I think there's some opportunity in oncology and non oncology space to actually use both the tempus <unk>.

In Western Europe, and beyond so I think those are more intermediate to long term plans.

Thanks, Laura.

Alright next up we've got a question from Lucille M, who ask what do you think about zero being founded and how much are they a competitor for recursion great.

Speaker Change: And the helix data along with our underlying data to get a sense of how these genetic.

Great question, So for those who don't know Theres, an exciting new Tech bio company with a great cast of characters that got announced a couple of weeks ago.

Speaker Change: These gene networks really work, knowing how they're perturbed in the context of oncology settings, and how they're perturbed in the context of non oncology settings, I think will give us a really robust a field from which to work robust substrate. You know it takes a while to take a discovery program and get it into the preclinical space, but rest assured maryann Gil as weak.

They've got significant funding really Marc Tessier Lavigne Ah.

And others, who are leading that organization.

There's a lot of disease that needs to be treated needs to be cured. So we welcome everybody to this space and our belief is that the biopharma industry in a decade is going to look a lot more like scaled versions of companies like recursion.

Speaker Change: Those programs into IND, enabling studies, we look forward to being able to share quite a bit more.

Speaker Change: Alright next we're gonna go to Laura who asks about our London office. What are we what are we looking for in the London Office why are we opening our London office and what our international growth plans beyond that a great question, Laura we like to operate in cities in communities, where we feel like there's an arbitrage where there's great talent.

Than it does today and so we welcome companies likes era to the space, we look forward to potentially collaborating with those companies competing with those companies. What I can say is that we believe in tech bio the primary bottleneck will be data.

We're seeing that we're data exists companies are making extraordinarily rapid progress with computational tools like machine learning and AI and where data is sparse it's much much more difficult and so what we think zero will have to do is generate in aggregate high quality datasets to make to make progress there and the reality is that.

Speaker Change: And maybe fewer companies that are leveraging that talent, we're based in Salt Lake City. We've got fantastic teams really focused in software engineering in Toronto and other related areas.

Speaker Change: Digital chemistry as well, we've got a great team in AI and AI research in Montreal, We've got a fantastic team in San Jose Milpitas with our in vivo facility in London and felt like an opportunity for us to accelerate our computational biology talent, we think the U K has done a really tremendous job of training.

Cells take time to grow Organoid to take time to grow and so we know that they've got an incredible team and we look forward to seeing how they start to work in that space continue to work to build the right datasets.

And certainly from our perspective, the more of them area, we look forward to leading the space and we're so glad to see so many super competent companies, joining joining us and others as we move towards what we see as an inevitable future.

Speaker Change: <unk>.

Speaker Change: Looks at the intersection of data science, and computation with biology, and chemistry really probably ahead of the universities in the U S. In terms of that integrated training and we add nearly 300 applicants in just the first couple of days, when we announced our London office for just a couple of dozen positions that we posted and these were extraordinarily talented folks.

I think I'm going to do two more here. We've got a question from Hamid Oh, Gosh, We who says my daughter has batten disease ceiling six an ultra rare genetic diseases are crushing willing to help labs, who are interested in helping these kids since we all know that pharmaceutical companies would not work for 35 patients and how to establish that kind of relationship.

Speaker Change: So we feel like that that is already paying off with fantastic talent in London as far as other international plans. You know I think that office is probably going to be a fantastic step for US internationally. We're still only a team of 530 or 540 folks. So I don't think Youll see us do a lot of additional international.

Thanks, Amit for the question My Heart goes out to you and your daughter your family everybody else with batten disease, and everybody else with a rare disease I think recursion believes that by building maps of biology by decoding biology, there will be a path forward to working across many of these diseases. We have a track record of working with.

Speaker Change: <unk> growth in the in the near term, but certainly as the company begins to move into development begins to scale our development ambitions, maybe even thinks about commercialization in the intermediate to long term will have an opportunity to grow in places like Asia.

Patient groups, you can reach out to us via partnering at <unk> Dot com and get connected to our patient advocacy team. There are scenarios, where we have used our maps to work directly with patient advocates to try and advance programs forward.

Speaker Change: Western Europe and beyond so I think those are more intermediate to long term plans.

Speaker Change: Laura.

Laura: Alright next up we've got a question from Lucille M, who ask what do you think about <unk> being founded and how much are they a competitor for recursion Greg.

And ultimately we do believe that companies like <unk> and others as tech bio comes into the space.

Greg: Great question, So for those who don't know Theres, an exciting new Tech bio company with a great cast of characters that got announced a couple of weeks ago.

Even if we don't have a clear hypothesis today around AR seal on section I don't know I don't have the map pulled up right now, but even if we don't have a clear hypothesis around <unk> six today or other areas of batten disease that these kinds of approaches. These these scaled approaches are are going to be really really exciting.

Greg: They've got significant funding really Marc Tessier Lavigne Ah.

Greg: And others, who are leading that organization.

Greg: Theres a lot of disease that needs to be treated needs to be cured. So we welcome everybody to this space and our belief is that the biopharma industry in a decade is going to look a lot more like scaled versions of companies like recursion.

In the medium to long term and I know that's no consolation to you and your daughter today, but my hope is that in five to 10 years, it's not going to be hard to see a biopharma company working across diseases that have 35 patients where maybe even less.

Greg: Than it does today and so we welcome companies likes era to the space, we look forward to potentially collaborating with those companies competing with those companies. What I can say is that we believe in tech bio the primary bottleneck will be data.

Thank you so much for your question and again reach out to a partnering at <unk> Dot com.

Alright, and finally, we've got a final question here.

Greg: We're seeing that we're data exists companies are making extraordinarily rapid progress with computational tools like machine learning and AI and where data is sparse it's much much more difficult and so what we think zero will have to do is generate in aggregate high quality datasets to make to make progress there and the reality is that.

There's a question about my beard, which I'm going to not answer.

And I will move on to a question from but thank you to allocate bank of America.

For the question about my Beard I'll go to Amir Shaheen, who asked what's on the wish list. The next big pieces of the puzzle that we need to get put in place and the wider community over a five year horizon and a 15 year horizon in order for us to progress as fast as possible and using state of the art computation for drug discovery Amir I think it really comes down to the datasets and.

Greg: Cells take time to grow Organoid take time to grow and so we know that they've got an incredible team and we look forward to seeing how they start to work in that space continue to work to build the right datasets.

We believe that ultimately to to fully understand biology people are going to need to build out really deep broad datasets and you're not going to need to build out hundreds of these you're going to need to build out a dozen or two dozen technologies, maybe it's for nomex proteomics metabolomics lipid ohmic transcript telmex.

Greg: And certainly from our perspective, the more of them area, we look forward to leading the space and we're so glad to see so many super competent companies, joining joining us and others as we move towards what we see as an inevitable future.

Greg: I think I'm going to do two more here. We've got a question from Hamid Oh, Gosh, We who says my daughter has batten disease ceiling six an ultra rare genetic disease is encouraging willing to help labs, who are interested in helping these kids since we all know that pharmaceutical companies would not work for 35 patients and how to establish that kind of relationship.

In vivo mix at some scale alongside predictive add me datasets talks datasets alongside automated synthesis and on the large molecule side moving in the direction of other modalities RNA eye therapies antibody therapies other kinds of gene therapies, I think there will be.

Speaker Change: Thanks, Amit for the question My Heart goes out to you and your daughter your family everybody else with batten disease, and everybody else with a rare disease I think recursion believes that by building massive biology by decoding biology, there will be a path forward to working across many of these diseases. We have a track record of working with.

A dozen or two dozen scaled technologies and the company, who can bring together the highest number of those over the next five to 10 years in a disciplined and robust way is going to start to be able to pull out compounding efficiencies. So that even if you only make each step of drug discovery and development, 5% 20 <unk>.

Speaker Change: Patient groups, you can reach out to us via partnering it recurs in dot com and get connected to our patient advocacy team. There are scenarios, where we have used our maps to work directly with patient advocates to try and advance programs forward.

Sent better than it was before and you start layering. These technologies together is I think recursion is really doing at least my belief more and better than any other tech bio startup in the space Youre going to start pulling together these compounding efficiencies and that's going to create this flywheel of momentum and opportunity and of course, we've got program.

Speaker Change: And ultimately we do believe that companies like <unk> and others as tech bio comes into the space.

Greg: Even if we don't have a clear hypothesis today around AR seal on six and I don't know I don't have the map pulled up right now, but even if we don't have a clear hypothesis around sale unfixed today or other areas of batten disease that these kinds of approaches. These these scaled approaches are are going to be really really exciting.

<unk> that are going to be reading out from our first generation platform over the coming quarters. We're excited then for a second generation of molecules to start reading out after that and we hope a third or fourth or fifth generation at each stage, we'll be able to demonstrate higher scale lower cost more rapid translation of these programs.

Greg: In the medium to long term and I know that's no consolation to you and your daughter today, but my hope is that in five to 10 years, it's not going to be hard to see a biopharma company working across diseases that have 35 patients or maybe even less.

And ultimately the biggest lever will be probability of success as you all know 90% of drugs fail in the clinics today from start to getting to the market and if we can get as an industry to 80% failure, and then 70% failure, and then 60% failure, where going to dramatically improve the access to medicines.

Speaker Change: Thank you so much for your question and again reach out to a partnering at <unk> Dot com.

Speaker Change: Alright, and finally, we've got a final question here.

And dramatically reduce the price of medicines over the coming decades, and we want to make sure that we are doing an experiment to ask and answer whether the kinds of tools that we're building can help lead out with that kind of vision. So watch for us to continue to build the vertical with small molecules and then as we make a lot of progress in that space, we start to.

Speaker Change: There's a question about my beard, which I'm going to not answer.

Speaker Change: And I will move on to a question from but thank you to allocate bank of America.

Speaker Change: For the question about my Beard I'll go to Amir Shaheen, who asked what's on the wish list. The next big pieces of the puzzle that we need to get put in place and the wider community over a five year horizon and a 15 year horizon in order for us to progress as fast as possible and using state of the art computation for drug discovery I mean, I think it really comes down to the datasets and.

Successes in that space, you can see recursion thinking about moving into complementary modalities as well. So we can go after a broader range of diseases, both with our internal pipeline and with our Biopharma partners.

Speaker Change: We believe that ultimately to fully understand biology people are going to need to build out really deep broad datasets and you're not going to need to build out hundreds of these you're going to need to build out a dozen or two dozen technologies, maybe it's for nomex proteomics metabolomics lipid ohmic transcript telmex.

Alright, thanks, everybody, it's been fantastic to connect with you all for these 35 minutes here at our Q1 2024 earnings. Please follow us on social please post questions at our future learnings calls and please engage with us at conferences and in other ways. We're so excited to be having this conversation with all.

Speaker Change: In vivo mix at some scale alongside predictive add me datasets talks datasets alongside automated synthesis and on the large molecule side moving in the direction of other modalities RNA eye therapies antibody therapies other kinds of gene therapies, I think there will be.

Have you and to be leading the <unk> as we move biotech into tech bio thanks, everybody and have a fantastic evening Bye bye.

Okay.

Speaker Change: A dozen or two dozen scaled technologies and the company, who can bring together the highest number of those over the next five to 10 years in a disciplined and robust way is going to start to be able to pull out compounding efficiencies. So that even if you only make each step of drug discovery and development, 5% 20 <unk>.

Okay.

Wix for my business because of the massive Skype functionality and freedom of creation.

Speaker Change: <unk> better than it was before and you start layering. These technologies together is I think recursion is really doing at least my belief more and better than any other tech bio startup in the space Youre going to start pulling together these compounding efficiencies and that's going to create this flywheel of momentum and opportunity and of course, we've got program.

Being able to integrate and change and develop them and pay for it.

Range of products and services Tonight, Jay over monthly is essential it gave me time back in my day gas gathering a natural protects it really just made everything feel more teeth.

Speaker Change: <unk> that are going to be reading out from our first generation platform over the coming quarters. We're excited then for a second generation of molecules to start reading out after that and we hope a third or fourth or fifth generation at each stage, we will be able to demonstrate higher scale lower cost more rapid translation of these programs and.

Speaker Change: Ultimately the biggest lever will be probability of success as you all know 90% of drugs fail in the clinic today from start to getting to the market and if we can get as an industry to 80% failure, and then 70% failure, and then 60% failure, where going to dramatically improve the access to medicines.

Okay.

Speaker Change: And dramatically reduce the price of medicines over the coming decades, and we want to make sure that we are doing an experiment to ask and answer whether the kinds of tools that we're building can help lead out with that kind of vision. So watch for us to continue to build the vertical with small molecules and then as we make a lot of progress in that space, we start to.

Speaker Change: Successes in that space, you can see recursion thinking about moving into complementary modalities as well. So we can go after a broader range of diseases, both with our internal pipeline and with our Biopharma partners.

Speaker Change: Right. Thanks, everybody, it's been fantastic to connect with you all for these are 35 minutes here at our Q1 2024 earnings. Please follow us on social please post questions at our future learnings calls and please engage with us at conferences and in other ways. We're so excited to be having this conversation with all.

Hi, everybody I'm, Chris Gibson, co founder and CEO of <unk> and I am really excited to welcome you to our first ever learnings call here at <unk>.

So what is the learnings call and why are we starting this practice now a traditional learnings call has a lot of value, but over the years I think these have become extraordinarily scripted frankly quite boring in many cases and hard to access for all of the stakeholders that we want to be able to speak to learnings.

Speaker Change: You and to be leading the tech <unk> as we move biotech intertek bio thanks, everybody and have a fantastic evening Bye bye.

Speaker Change: Okay.

Our interpretation of a traditional earnings call, which we feel is more authentic so I will not be scripted today I'll just be working off of the slides in front of me adaptive and we hope easy to access and please if you have suggestions on how we can make this better going forward. Please send them our way.

Speaker Change: Okay.

What I would also say is that we've chosen to initiate our first learnings call. This at this moment at the start of 2024, because as we look ahead at the future of precaution, the milestones and catalysts coming before us are going to be coming fast and furious and we want to make sure that we have a robust mechanism to reach out to all of our stakeholders on a quarterly.

Speaker Change: Okay.

Cadence and to be able to share all the incredible work that we're doing here at <unk> with you.

So to frame, where we are today, where we've been and where we're going I want to start by going back really a decade going back to the origins of tech bio <unk>.

One decade ago.

Speaker Change: Okay.

And it was a really interesting time in the early 2000 tens you saw technology companies coming into a wide variety of industries and leveraging a pretty straightforward playbook to bring fundamental new advances from how we get around cities to how we think about our preferences for digital media to how we even think about what.

Speaker Change: Yes.

We want to order and what these companies did was quite straightforward they used technology to capture high dimensional data to create a digital record of reality and it's important to note that the data that they collected was rich very very rich in high dimensional they aggregated and digitize that data and then leveraged al.

<unk> to make predictions across all of these massive datasets and most important of all they went back into the real world to test those predictions. So whether that's telling you to turn left instead of right, whether it's telling you to byproduct a instead of product b or to watch television show X or Y. These algorithms could be tested in there.

Ability to predict the right outcome in a real setting.

But in biology. This has been extraordinarily challenging there are so many roadblocks to aggregating and generating the right data to be able to map and navigate this complex system of biology and chemistry.

There are three primary drivers of that first this world is very analog standard it was more so in the 2000 tens, but it still is in some ways. Today. There are still crows, who send you scanned pdfs or printouts with handwritten notes.

And in the Biopharma industry, there's a tremendous amount of data hundreds of petabytes of data, but that data was collected in a way that wasn't built for the purpose of machine learning and so it's often siloed on legacy servers, it's often built without the right kind of high dimensional nature or the right kind of metadata to make it easier to extract the connections across them.

Between all of those different data and then of course, there's the public datasets that we and others use but as you all know theres, a reproducibility crisis and there are real challenges because just like in the pharma data, there's not enough metadata and not enough relate ability of this data across all these different publications and data sources.

And so it's very very challenging in the biopharma industry to aggregate and generate the right data, but what we and other companies who are today, leading tech bio saw in early 'twenty tens with an opportunity we saw exponential improvements across five main areas. The first was the cost of storage. So in the early 20 turns we.

We're at the end of a 40 year cycle of precipitous decreases in the cost of storage and this is important because a company like us ever Kerjan today with over 50 Petabytes of proprietary data has to be able to pay to store all of that data. We were seeing a radical increase in availability of compute will talk more about our supercomputer a little bit later.

We were seeing an increase in accessibility and flexibility of automation tools that allowed us to pioneer in industrialized a new kind of Olympics using robotics, we were seeing a renaissance of new biological tools like CRISPR and then of course the field of AI was making extraordinary strides as we took 20.

Years of learnings and really invested in billions of dollars across the tech industry to move from expert systems into this neural net modern AI age.

Okay.

Dream only appears retails.

So Mr <unk>.

Yeah.

Yes.

Got to happen.

Oh, my gosh move on their own.

Yeah.

Hum.

On what's about food comes by itself did you mean by a huge team of developers.

I'm, telling you it is.

Yes.

Yeah.

Okay.

Our maintenance capital of millions of developers and powered by the right tools.

Just brings I D.

Yes.

The captain.

Road.

Yeah.

And now fast forward to today, where recursion is right now leading tech bio we are taking that same formula that was so obvious across the technology companies of the early two thousands and 2000 tens and deploying it now across the Biopharma industry, where as I said before the data is so hard to generate and so hard to aggregate.

But we are doing it here at <unk>, we built a massive automated platform, where we can profile biology across human cells rodent cells in vivo systems, and even patient data, we can extract that data in high dimensional space aggregate. It and then train algorithms on our supercomputer.

In cloud computing resources to make predictions in this is the most important part more than any other company in this space I believe we are set up to take the predictions from our algorithm and test them back in the lab and creating that virtuous cycle of learning and iteration.

Is the recursion O F. It's what we've been building for the last decade, and it's what we see positions us the data the technology together in this virtuous cycle to really define and lead the tech bio space in the decade going forward.

But we're not just building at one point in the drug discovery and development process. It takes hundreds of steps to discover and develop a drug in recursion. Today is building. These virtuous cycles of wet lab and dry lab of learning and iteration at points from how we connect patient data into our targets to how we optimize chemical compounds.

How we translate these programs and now early work and how we even identify the right patient cohorts to drive our programs into the clinic I think more than any other company in the space really building the full vertical tech bio solution.

And that means that we are leading tech buyout in 2024 across three primary areas, our internal pipeline, our partnerships and our platform <unk> is leading our first generation programs five phase twos, either enrolling or soon to enroll patients that are really focused and capital efficient niche areas.

Our biology, and we're excited to have second generation programs that are leveraging some of the tools that we have built or added to our platform in just the last few months moving to the clinic as well if we build this platform right every generation of programs will be better than the last but it's not just our internal pipeline. We're also learning from and working with partners across both.

With Bayou and tech on the biology side, we're partnered with Roche Genentech in neuroscience, and and one oncology indication and then also partnered with our colleagues at buyer in precision oncology.

But unlike many other companies in this space, we not only have the therapeutic partnerships. We also have partnerships across data with companies like tempus across compute with companies like Nvidia and across chemistry with companies like enemy and it is this cross credentialing nation of technology partnerships and biology partnerships that we believe.

Sets us apart and all of these partnerships and pipeline are based off of the recursion platform today over 50, petabytes of proprietary biological and chemical data spanning human cells to wrote himself to model organisms to human patients and in order to make use of all of that data substrate ever cogent.

Today, we now own and operate the fastest supercomputer in the Biopharma space and in order to take the predictions from the algorithms that we generate on this computer and test them in the lab, we have industrialized and automated multiple levels of AUM ex data generation that recurs in on our <unk> platform. For example, we're able to do.

More than 2 million experiments in any given week.

And so before I talk about what we're looking out to in terms of our near term catalysts and milestones for <unk> I want to take a moment to just look back at 2023 and I wanted to do this because I think it was one of our very best years amidst the challenging capital market environment. This team delivered on our pipeline our partnerships and our platform and so we're going to go through just a few of the highly.

First I'm going to start back in May where we announced simultaneously on the same day the dual acquisitions of cyclical a digital chemistry company, that's based in Toronto and valence, a cutting edge AI laboratory for drug discovery, that's based in Montreal, and we were able to fully integrate the cyclic a team in just 90 days and in a few minutes I will share with you.

Some of the output from that acquisition that led to us advancing and improving our programs within just a few months of signing that deal on the valent side I'll show you low later, which is our large language model workflow orchestration.

Engine and this has really been driven by the Valens team and I will set the stage for how we see a new direction for how Biopharma is going to access all of these incredible new tech bio tools.

In June we announced that our first clinical trial Sycamore. This is a trial for the first therapeutic candidate to be advanced by any industry sponsor into phase two for cerebral cavernous malformation and I'll remind you that this is a massive area of unmet need. This is a disease that affects roughly six times the number of patients as cystic fibrosis.

And yet we are the first with an opportunity to be first in disease. This program was fully enrolled in June across 62 patients in three arms and one thing that gives us a lot of confidence about the tolerability of this molecule if it today as patients finished their 12 months on therapy.

The vast majority continue to opt into our long term extension study and so we will be reading out the topline phase II data in Q3 of this year. This will be our first real POC readout and we're really excited about the opportunity not only to potentially drive forward in exciting medicine for an area of significant unmet need but also regardless.

The outcome of that study to learn and put that data back into our platform. So that the next generation of molecules can be even better.

Then in July a month later, we announced our collaboration with Nvidia. This included a $50 million equity investment and with our partners at Nvidia, We're working on advanced computation. So foundation model development, we've got priority access to compute hardware, which I'll talk about later and the D. G X cloud resources and we talked about.

I'm about the potential for us to put some of our tools into their bio Nemo marketplace and in fact, just last month in January at the JP Morgan Health Care Conference. We released the first third party tool to exist on in videos Bio Nemo platform that was our Phenom Beta Foundation model.

In January of 2024, so very excited about this ongoing collaboration.

One month later in August we were able to deliver a demonstration of how we leveraged the may acquisition of cyclical and our brand new partnership with Nvidia to drive a real value into our platform, we were able to predict the protein ligand interactions for more than 36 billion compounds from the enemy real space.

Across about 80000 predicted binding pockets spanning the human proteome and.

What this did was generate a large it's in silica data layer for us at synthetic data layer. So when we find a new target or an initial hit we can immediately prioritized that target based on a potential mechanism of action and we have already advanced multiple programs terminated multiple programs or change the course of multi.

Programs using this exciting new technology, so we're really.

See it is fantastic to have the complementarity of this functional machine learning algorithm alongside our or this physical machine learning algorithm alongside of our functional biology based platform here at reversion.

Then in September.

<unk> announced the phase one study results for Rex $39 64 in.

C Diff colitis, the molecule was safe and well tolerated multiple doses up to 900 milligrams. There were no <unk> and no discontinuation that were related to treatment and along with a favorable PK profile. This gave us the confidence to advance this new chemical entity towards a phase II trial, which we will initiate later in 2024.

Then back to our platform in September we announced our first foundation model, we call Phenom one it's the world's largest phenomena foundation model that we're aware of and I want to take a moment just to talk a little bit about this because I think it's really exciting, especially given all of the talk around large language bottles in the background in a large langer.

Which model one trains a neural network to predict the next word in a sentence or in a in a in a paragraph and we've done something similar here, but instead of using written language. We're using the language of images of human cells and what you can see on the left is an image, where we've massed 75% of the cellular.

Image and we've trained a neural network to predict what the rest of that image would've looked like that's the middle row here and you can know the middle column and what you can see is that our neural nets got really good at doing this you can almost not even tell the difference between the phenom, one reconstructions and the original image, but we're not in the business of reconstructing masks.

Images at recursion, that's just a training task and like in a large language model, where the ability to predict the next word in a sentence led to these emerging features that almost gave us a sense of a rational thought in chat GPT and other sorts of settings. We're seeing emergent features from these foundation models so against.

A wide variety of benchmark tasks in drug discovery. These sorts of bottles are giving us state of the art performance to rediscover known biology to make predictions about admin talks and beyond one of the things that was most interesting about this work, though was that we were able to demonstrate that the scaling hypothesis holds in the world of biology.

We were able to demonstrate that that is that the bitter lesson holds true and that one must have more data and more more compute all else being equal in order to build a better model.

So based on that just two months later, we announced with our partners at Nvidia that we were expanding our supercomputer, which was already the fastest supercomputer wholly owned and operated by any Biopharma company with another 500 and for Nvidia H one hundreds and this is a picture of the team just a week or two ago, where these these H one hundreds of <unk>.

Right on site and we believe when the system is up and running it will not only be the fastest supercomputer in the biopharma space, but it has the potential to be one of the fastest supercomputers.

<unk> run in any industry.

So we're really really excited about the potential to get this thing up to speed and humming.

But going back to our partnerships in October we also announced that Roche had exercised the first program under our collaboration this program in the context of oncology and this was fantastic less than two years after signing that collaboration to already have a program advancing forward with our partners and we hope and expect that this is the first of many options to.

Come across this partnership and others.

In November we then announced another partnership this time instead of just generating data at recursion partnering with Tempus to aggregate. What we believe is extraordinarily high quality patient data into our platform access to the DNA and RNA sequencing datasets and clinical records for over 100000 patients that we can now.

Grain causal AI models on using the recursion OS.

That gives us now access to over 50 petabytes of proprietary biological and chemical data that we've either generated in house or partnered with companies like tempus to bring in place and I'll talk more in a minute about how we're already leveraging this partnership to drive value in our platform.

Also in November we announced an update to our partnership with Bayer focusing on precision oncology and I think it's important to note that with this update we were able to more than double our per program milestones, which I think is a strong signal about bayer's excitement around what we're building and I know the teams are already hard at work together at Bayer and at recursion.

To drive forward some of these initial new oncology programs together.

Yeah.

Coming out of that same a partnership before it moved to precision oncology. It was focused in fibrosis and there was a program that was part of that that we thought was just too good to let go to waste and so we were able to negotiate with our colleagues at Bayer to in license. This program, which we call target Epsilon, which we believe is a novel target and the contact.

The fibrosis and we are driving this program forward very quickly in fact, we're announcing with today's earnings that this is now in IND, enabling studies that recursion. So we've already advanced it inside of our own internal pipeline.

And finally in December we also crossed the threshold of having generated over one trillion neuronal Ips C cells since 2022 and based on the publicly available data. We believe that this makes us the world's largest producer of high quality neuronal Ips cells and this is but one example of the way our T.

Team is working with complex biology co culture systems, a wide variety of variety of biology to drive our platform forward into new exciting areas like neuroscience.

All of this underlying our pipeline, which as I shared earlier, we believe is the most robust deepest and broadest in the tech bio space and we are now looking forward at 2024 with this learnings call setting the table for a number of important catalysts that are coming up first our phase two top line readout for CCM in Q3.

<unk>, then a preliminary safety and efficacy readout for Nf too in Q4, and then in the first half of 2025, a preliminary safety and efficacy readout for F. T. The initiation of our phase II program for C. Diff colitis later in 2024, and then another phase II safety and preliminary.

Efficacy readout in the first half of 2025, so it recurs and really beginning with this third quarter in 2020 for setting the table for what we hope can be roughly quarterly readouts that we hope will help propel the company and the platform forward beyond. These early first generation programs, we've got our Epsilon project and our RPM 13th.

Project, which are the first of our second generation of programs, making use of some of our newest tools and we've got more than a dozen discovery and research programs in oncology or with our partners coming behind those I think hundreds with Liberty mutual.

Liberty Liberty.

Yeah.

Now before I talk about where we are today and what we see as catalysts in the near term beyond just our pipeline I want to Orient you to the broader trajectory of the space of <unk> at least as we see it and to do that I have to go back a ways again to the early days back to the 20th turns from companies like brokers and were founded and all of these companies reach.

Really made their start with a point solution and we're no different we were scaling industrializing and pioneering a new kind of own mix based on images of human cells to try and understand and explore biology and since that time, we've actually seen that our work in this space has just continued to.

Grow in complexity today, we can leverage our automated platform on pheno mix to generate more than $2 2 million experiments worth of data every week, we leverage extraordinary foundation models like phenom, one that I talked about earlier to make predictions about the relationships across more than five trillion.

Biological and chemical context. This is an extraordinary extraordinary feat and it's based on broad biology over 50 human cell types that we've explored roughly 2 million chemical compounds whole genome CRISPR knockout. This is really really exciting work that we continue to push the limits of but.

This is but one step in the recursion Oh asked today, while we started with Pheno makes it is now one of many steps spanning patient connectivity all the way to the clinic and while I wish we had time to go through each one of these I'm just going to focus on a few of these areas that I think are important to illustrate some of our focus on building these virtuous cycles.

And the first of those is D M P K.

Our D N PK platform is now up and running at <unk>. This is a highly automated platform, that's allowing us to execute three critical assays across both human and rack contexts. We can do nearly a thousand compounds a week on this automated platform and this is great. Because we can profile of the molecules that are moving through our internal pipeline or our partnership pipeline.

But what's more we're using the majority of this platforms bandwidth to actually profile many diverse compounds to build the data substrate on which we can train additional state of the art predictive add me and Tox models and it's this virtuous cycle of learning an iteration of data generation and algorithm improvement.

That we think will differentiate us not only in target discovery with Pheno mix hit discovery with Pheno mix, but even in how we advance our molecules towards the clinic and it doesn't just stop in human or road in cells. We're building the same kind of tools in model organisms in our vivarium, we have over a thousand cages with cameras and other cell.

<unk> that allow us to extract much richer high dimensional data from each one of these animals and this means we can use fewer animals as we drive our programs forward and it means we can make decisions in real time, we can de prioritize and prioritize molecules based on digital Tolerability studies in real time, and this has already made a difference.

And both accelerating and leading to the faster termination of programs at recursion.

But it's beyond model organisms. It also goes to the ultimate model organism and that as humans with our tempus data, we're able to now aggregate patient data across oncology together with all of the wet lab data we've generated at recursion and in just about eight weeks since we've had access to this data this is <unk>.

Already led to our team combining our wet lab.

Data and the patient data, so forward and reverse genetics coming together and allowing us in the context of non small cell lung cancer two already identified multiple potential.

Drivers of disease that we are predicting our causal which in many cases have not yet been robustly explored in this space. So recursion now has a program that has advance just in the first eight weeks based on this kind of data and we're really just getting started.

But what's happening is that as we continue to build this full stack of technology tools and as each of these tools runs through its virtuous cycle of learning and iteration and has improved rapidly it's becoming increasingly complicated for anyone to keep up with the latest on each tool the right way to use each of these tools.

And we actually think this is going to be a problem across the industry as we and many others are building lots of different models and lots of different tools and so we wanted to address that together with our colleagues at valence laugh and we were able at the JP Morgan Healthcare conference. Both in the conference. We think for the first time doing a live software demo.

And also at the event, we co hosted with Nvidia to show off our low system. This is a large language model orchestrated workflow engine and what this is allowing you to you to do what our scientists and our partner scientists may be able to do with this technology. This tool is to use natural language to not have to be an expert.

Grammar to be able to access all of the tools to be able to design experiments the right way to order experiments and execute them on our platform to analyze data and visualize data using the latest tools at recursion and really this kind of technology is putting the power of the recursion OS at the fingertips of all of our scientists.

And partners.

And we see this trajectory as very similar to the early days of the late seventies and early Eighty's in the personal computer space you had a product like the Apple one on the left where you really have to be an expert user you have to be comfortable with this microprocessor board you have to be comfortable working at the command line in order to make.

Use of this burgeoning new technology and with subsequent Apple models, including Lisa on the right. We moved to a graphical user interface and this created really a renaissance in the ability of more people to be able to harness the power of compute and what we're building with low with the <unk>. We believe is akin to this but it.

Really a discovery user interface and we believe it's going to allow each scientist at <unk> and beyond to make more progress faster, it's going to mean that our teams are doing less of the toil and more of the thinking around our projects and it also means that these tools are gonna be accessible not just of scientists in biology, and chemistry, but the software engineers and data scientists.

As to BD and to finance and we think ultimately that's going to be fantastic for the field and we believe recursion is really leading out on this new trajectory for our industry.

So before I move to questions I wanted to just and with our near term milestones. The things that we believe we're going to hit over the next 12 to 18 months or sooner and I'll start with additional Ind's. We've got both our RPM 39 program and our target Epsilon program that we in licensed from Bayer moving towards the clinic, We've got me.

<unk> phase II trial starts axon, one or a P C and C. Diff that we will believe will be starting this year, we have multiple phase II readouts that I alluded to earlier and all of this on top of a healthy balance sheet with nearly $400 million in cash at year end 2023, and what's more we see the potential for significant runway.

<unk> options for our map building initiatives with partners and for additional partnership programs being optioned and beyond that we see the strong potential for additional partnerships in large intractable areas of biology, like cardiovascular metabolism, and immunology, where we expect robust upfront payments that will further extend.

And our runway and what's more we have an ATM open which we're using in a very very surgical way with the right investors at the right time in order to make sure that the company maintains a robust runway moving forward across all of these exciting catalysts and finally, we've got the potential both on the bio Nemo platform and through our low tool to.

Some of our data and some of our tools available to Biopharma and commercial users and there's the potential for some of that work to generate additional revenue as well. So I hope you're as excited about the future of tech bio as I am I Hope. This has been helpful for you to see the trajectory of the company through 2023 into 2024.

And how we see the future of our industry and with that I'm going to stop here and head over to answer some questions and these are being updated by our team alive. If you haven't had a chance to ask a question yet please log into the slight o'toole and do so now.

And it looks like the first question is from Morgan Brennan of CNBC and Morgan asks what is the reaction been so far from Drugmakers and others to low how big do you think this revenue stream could be for the company. Thanks.

Thanks, Morgan that's a fantastic question.

I would say the response has been really really robust we had many.

R&D heads of large pharma companies at R. J P. Morgan presentation, which we co hosted with Nvidia, we had Ceos of large companies, they're both attack and bio and what we heard from people is how do I get access to something like this.

And we are doing the work now to increase the robustness of the low platform, we're having conversations with potential partners around how we could put these tools in their capable hands in a way that would be helpful to <unk> and to the industry writ large as far as guidance around revenue I don't think we're going to give guidance around revenue.

In the near term what I will say is that we see a bigger opportunity in driving these companies towards really significant collaborations like the ones, we've done with Bayer and Roche Genentech.

As they see the power of a tool like low probably that's the bigger opportunity for us in the near term compared to sort of recurring software revenue, but we certainly will take all the revenue we can get if we're able to identify those questions.

Alright. Thank U next up we have a question from Alec Stranahan of Bank of America.

How do you plan to utilize low either internally or as an external offering how does this fit into your existing full stack capabilities. This is actually a fantastic question because I think it highlights something thats really important low internally at recursion is being used by certain teams on the BD side and elsewhere. It certainly is something we think.

Pharma could use but I'll actually go to a slide from our.

A slide from our other deck here.

To say that internally, we actually believe there's a step beyond alone where autonomous agents use a tool like low to drive discovery as opposed to individual scientists and I think this is a great example of this this is a plot of thousands of targets in.

In human biology, and what I'm showing you here on the Y axis is how we've used a large language model that is based on public datasets like the cancer dependency map open targets TCG et cetera, and we have profiled all of these different targets to assess their relevance in oncology.

Whereas on the X axis, we've used a large language model that's looking only at proprietary data internal to <unk> and so what you see on the top right is important targets like a P. I K three C. A BRAF M Tor Egfr et cetera, where we see a.

Our approved medicines for these targets in oncology, we see that these targets score robustly for oncology.

Relevance based on both the public data and <unk> proprietary data, but we see hundreds of targets in the bottom right. In this blue box that are now being automatically initiated as new pre programs at recursion without almost any human intervention based on our large language model scores and we see these as.

<unk> targets that have the potential to be totally novel and so Erik Hirsch and our scientists are just using low they're really using robust workflows that are highly automated and low as more of a tool that we see to collaborate with partners that we see to drive partnership progress through our pipeline.

Alright.

Next question is from Jesse Brodkin, who asks why did you choose temple erect 99 four for your CCM indication when the vitamin D data looked better in our preclinical screens. Thanks, Jesse for that question. There's a circulation paper that you all can read about this work and what we noticed was that both vitamin D and recognized nine four.

A robust response in the context of these preclinical models.

Rec 99, four as response with additive on top of vitamin D. So there was vitamin D. In the Chow of the mice and the rack 99 for treatment added to the effects that we saw and given vitamin D is a very safe widely available molecule that many people take in their in their everyday you get when you stand out.

The Sun as well, we didn't see a lot of added value in us, bringing that program forward, whereas bringing rack 99, four which was otherwise inaccessible to people. It was not approved not available forward. We believed there was the potential for additive benefit and that's why we've driven that program forward and we're so excited to read out the data in Q3.

Alright, it looks like next we've got a number of questions around our Nvidia collaboration the first from Harry Schoenberg at J P. Morgan, who asks what involvement will you have with Nvidia in the near future and going forward. That's a great question I'll go back to the slide on our in video collaboration here.

And just to reiterate.

That with Nvidia, we are really focused in three areas. Currently the first is advanced computation, we've been working with the team there for many years.

We think they are incredible and theyre, helping us take the algorithms that we're building and help scale them help tune them and theres not many people in the world who have a lot of experience training multibillion parameter models, but theres a great team at Nvidia that's done just that and so we are collaborating really closely on some of our larger models once more we've already.

Demonstrated the use of our priority access from Nvidia in our expansion of our bio hive supercomputer and of course, there is the potential for us to access the <unk> cloud resources in a in a priority way as well and then finally, we see the potential for us to put potentially additional tools on their bio Nemo marketplace. As we continue to develop these tool.

And who knows the collaboration with Nvidia is very very close.

And we know that our teams are constantly coming up with new ideas and we'll be excited to try some of those out with our colleagues there in the near future.

Next question is from Mark Smith, who asks describe the relationship and investment with Nvidia regarding AI in their products I think we've really hit on this one already so I will move on.

Okay. The next question is anonymous.

This is a good one why are insiders been selling shares each months do they not have confidence in the company.

That's a great question and I'm glad we're addressing it so.

I'll speak for myself, because I think most people look to the CEO when it comes to insider buys and sells and in 2023 Ah I traded a very small relatively small number of shares in fact, it was roughly about 4% of my holdings that were traded and so all of these trades were done.

On using can be five one pre planned sales and purchases and again I traded roughly 4% of my holdings. If you were to look at that of the Grand scale of just on our volume today all of the trades I did in 2023 represent roughly six or 7% of just the volume recursion traded today in the market and so I think.

While you see many of these sales many of these purchases across insiders. The reality is that the magnitude of these is relatively small and we're using these four are making sure that we've got the right diversification in place. This is my first job out of Grad School and so I have the vast majority of my shares that I've had from the beginning.

The vast majority of my shares that I had at the IPO and I intend to keep the vast majority of my shares moving forward because I definitely believe in what we're building here I've dedicated my life and my career to it.

Next up we've got questions in our fibrosis projects. So Alex Stranahan asks fibrosis has been a historically challenging area for development. This is true how is the asset you in licensed differentiated and what are the first disease areas of focus well Alex I really appreciate that question I'm not going to share the first.

Disease area of focus yet.

Because this novel target. We're working on we think has the potential to be useful in multiple different areas and so.

We're going to probably hold that information back from a competitive standpoint for a while what I will say is the differentiation here is that we used a very complex assay, we essentially looked for small molecules that we're mimicking the effective Penn traction too in a complex fibrocyte assay and what we saw.

It was a number of molecules.

Since then we've really optimized one of those molecules 1169575.

And an additional molecules that we're advancing as backups and we think this novel mechanism.

And if you knew the mechanism I could tell you more but we're not going to share. It yet has a lot of potential to modulate the immune response that could be broadly useful across this space. So we're aware of the challenging development space, We certainly could imagine partnering this program as we get into sort of the phase two.

A portion of the clinical trials, but we think this one is important and worth advancing because we're unaware of anybody else taking this target or this target class forward in the context of modulating the immune system to drive a reduction in fibrosis.

Alright, the next question.

It comes from Jesse Brodkin, who asked did recursion pay Bayer any money to obtain the fibrotic disease lead candidate from the collaboration.

So Jessie this program was advanced under our original fibrosis collaboration and specific disclosures around the financial terms can be found in the 10-K and we'll be filing that 10-K here in the next 48 hours or so so you can look there, but what I will say is we didn't have to pay anything upfront. There are some modest milestones that we think are very attractive as we drive this.

Program forward and I think both we and the scientific team at buyer are pretty excited to see what we can do with target Epsilon.

Alright. The next question back to Morgan Brennan from CNBC.

And Morgan asks what proof points can you share on AI ml in medicine, and our AI applications in drug discovery happening as quickly and effectively as you anticipated Morgan. It's a great question. So I will share that I'm, a founder and I don't think any foundry is ever satisfied with the pace.

Anything is advancing so I can say no things arent going as fast as I would've liked but I think if you look back at where workers and started in 2013, where other companies like us started and where we are today. We now have developed it recurs multiple tools that are state of the art in terms of target identification in terms of making add me in talks.

<unk>, we have a pipeline of five programs in phase two or nearing phase two I think we can be really proud of the platform. We built the pipeline. We built the partnerships. We've built some of our partnerships our not only our Roche Genentech partnership is not only the largest partnership in tech bio today.

It's one of the largest partnerships ever disclosed in Biopharma in terms of total kind of biomarker potential and so I think that while the next 12 to 24 months is going to feel to all of US like we've kind of under delivered we're on this sort of exponential curve, where if we look back in five to 10 years, we're gonna be amazed at how far <unk>.

Things go but the reality is like with any new technology. It takes time and if we run these virtuous cycles, and we get one or 2% better each time, but we can compound those efficiencies through many many cycles I think over time, we're going to see a fundamental transformation of biopharma space that over a decade is going to feel much more profound than most.

Believe today.

Okay.

Alright next up we have a question from Curtis Maxwell who asks what is the backlog of projects that are in the pipeline for AI analysis.

And what is the cost per project and duration typically.

So here, we can actually look at if youre, referring Curtis to our programs ever kirschman, we can share. Some of these statistics, we believe it recurs and that we're trying to shape. This traditional V of the biopharma industry into more of a T where one day, we will be able to take all of them.

Prior data and our algorithmic approach and predict the right molecule for each patient and drive it all the way to the market without any attrition now that T is going to be impossible to actually completely achieve but we want to move in that direction and you can see compared to the industry average were already starting to shape, our internal funnel to la.

Look more like that T unless like that V and what we're able to demonstrate so far across our programs is that our cost of <unk>.

And our time to validated lead significantly outperformed the industry averages whats next up is that we hope that we're going to be in a position to demonstrate at least meeting the probability of success of the industry averages with a faster time and higher scale for the size of our company and every generation of future programs, We hope will build.

On that and one day, we hope to be able to demonstrate to the industry that we can increase the probability of success of our programs and we can drive them forward not only in areas of unmet need in rare disease and oncology, where we can be first in disease potentially but also one day to leverage this platform to fast follow at <unk>.

Scale to be able to take programs that recursion that we can drive extraordinarily quickly based on the incredible science, that's being done elsewhere in the industry. So a lot of good work to come there.

Alright, it looks like we've got another question from one of our analysts here given the complexity and layering of data keeps growing on your platform. How would you define a proof of concept in a constantly moving platform. Yeah. That's a great question Gil and I think this speaks to a difference in mentality across the tech and the bio industries.

We believe that these virtuous cycles of learning and iteration must always be running and that increases the challenge of keeping up with the latest tool. The latest version of that tool, but we want to make sure that every program ever Kerjan uses the latest generation of every tool that we're building and that's why we talk about the generations of our clinical pipeline the first.

Generation programs, which are by and large focused in rare genetic diseases before we had a chemistry team. So most of those first generation programs are actually molecules, where we used our ml and AI platform to identify a new opportunity for known chemical entity and you'll see in our second generation, you'll you'll start to see the layering in.

New chemistry, and digital chemistry tools to these programs as we advance them forward and as we run a third generation in the fourth generation in the future I think you'll see we hope that this platform learns that this platform improves and that every generation of programs will have on average and increasing probability of success.

And we hope increasing impact.

Alright, let's go now to some investor in revenue questions. We've got a question here from Eric Joseph with JP Morgan, how should investors generally be thinking about the company's business model. This stage, Eric that's a it's a fantastic question at the end of the day in our industry the currency of impact of the currency of success.

Yes, its assets in the clinic and I think that's why it recursion has not just focused on building software as a service not just focused on our partnerships, but has a robust internal pipeline that we're advancing in.

A small areas of small niche corners of biology with high unmet need and partnerships, where we can go after large intractable areas of biology.

We are always doing business experiments that recursion low is a business experiment phenom one was a business experiment.

And we don't yet know how those will drive our business model per se, but what I am confident in is it recurs and we'll always be focused in bringing new composition of matter into areas of biology with high unmet need or where we can drive down the costs of expensive molecules that have been advanced into the.

Market.

So I think you can count on that being at the core of what we're building at recursion, but we're going to do all of that with a much more tech focused mindset and I think many other companies in this space.

Alright back to Gill one of our analysts do you anticipate that overtime more value will be created from the companys internal pipeline or through its partnerships well Gail if we're talking about long term I believe recursion, it's going to generate much more value from our internal pipeline that our partnerships, we expect to generate significant value in our partnerships today.

We sign these partnerships with Roche Genentech and with Bayer because we saw them as having transformational potential for patients and the potential for extraordinary impact in areas of high unmet need but as each of those.

The partnership's finishes, we expect to have learned what we need to as a company to be able to build our own internal pipeline into those more complex intractable therapeutic areas and until every disease has a treatment. We won't rest until I think you can count on <unk> internal pipeline being a robust primary drive.

<unk> of our growth if we were to look out over the intermediate and long term.

Alright back to Eric Joseph J P. M. What's envisioned as its earliest and most significant lines of product revenue I assume that Eric's talking here about some of our software tools like low.

Eric we're having lots of discussions with Biopharma companies today about how we might integrated tool like low and our teams that recursion with them I think it's too early to talk about the significance of these lines of product revenue I don't think it's too early to talk about how recursion, leading the field with tools like LOE is helping.

Paul the industry forward partnering with extraordinary companies like Roche Genentech and buyer to help move the entire industry forward and I think over time, whether it's through the software offerings themselves or whether it's through new chemical entities that we discovered with our partners or in our own pipeline I think we're going to drive a tremendous.

Mount of product revenue leveraging these tools.

Okay.

Alright next up we have a question from Corrine Harrison, who asked when will the company be profitable well. That's a great question I think we see the opportunity before us.

As a multi trillion dollar opportunity with profound potential impact for patients. There are few industries today, where despite hundreds of thousands of really incredible scientists working really really hard on average our industry still fails, 90% of the time in the clinic and what's more I think.

There are roughly 20 or 25, biotech and biopharma companies today with market caps above $100 billion.

That kind of lack of.

Uh huh.

Condensation of these companies are I think is pretty unique to biopharma and so we believe that if you look out 10 to 20 years, there will be a much smaller number of biopharma companies and those companies will look much more like <unk>.

Does today than they will look like a traditional biopharma company and we hope and expect to be one of those and what that means is that we're going to lean into growth in the coming years, we're going to be good stewards of our capital, but we're going to lean into growth and so because we see the magnitude of that.

That opportunity, while we hope to decrease with the upcoming milestones and revenue we hope to decrease the losses on a quarter by quarter basis in the intermediate term in the long term I don't think we're going to lean into into maximizing profitability. Because we think there's a multi trillion dollar prize and impacts for hundreds of millions or billions of.

<unk> on the line over the coming decades.

Alright next up we've got a question from Juan Fernandez, who asks what is the company vision what daily actions are being taken to achieve it. One. This is a great question. We believe that it is the biology and chemistry or deterministic.

That with the right data and the right technology tools, we will be able one day to predict how any biological and chemical interaction operate not only in human cells, but in the human organism and beyond the human organism in any living organism and our vision is to be the company that digitizes the space that moves from wet lab.

One day entirely to dry lab, where our experiments are done only to validate the predictions we make it scale and if we can achieve that vision I think we have the potential to be one of the most impactful companies in the world and so how do we manifest this everyday well we have a recursion mindset that we teach our team.

We have events like the coding recursion I just got back from one last week, where we bring new and tenured employees together for a couple of days to talk about how we can focus on the experiment. We are here to run we don't want to play the game everybody else's, playing because we know what the probable outcome is we want to play a different game, we want to test.

This idea that there could be a different way to discover and develop medicines and so we pushed that into every person that recursion, we've been pushed that into our partnerships pushing our partners to adopt new tools to adopt our workflows and so it's very front and center at recursion and we lean into that vision every day, we still have all hands.

Every week ever Kirschman, I'm still a presenter at all hands as often as I can be.

We bring together people to really lean into that vision and we're not apologetic about it we believe that somebody has to be trying to make this space not just a little bit better, but a lot better and we're thankful not only that recursion is doing that whether there are many other tech bio companies and many other companies in the Biopharma industry, who are making big bets.

On how the future could look extraordinarily different from how it looks today.

Alright.

Next question from Steve Dechert, do you have a rough timeline of when you might submit an IND for target Epsilon. Thanks, Steve we're entering IND, enabling studies at this time, we just advance that program here in the last week or so so I think we'll be able to give you a better timeline for that in the coming quarters, but I know the team knows that I'm never satisfied.

That speed with quality is what we're aiming for with that program and every other one at <unk>.

Alright, I know we have a question from Steven Greenwood, who asks would you consider looking at multiple sclerosis in the issue of remodeling. This Asian, Stephen That's a great question and certainly I can't talk about the specific areas of neuroscience that we may collaborate on with Roche Genentech, but what I will say is that an important limitation of the <unk>.

Platform that we built today at recursion is that it is not yet built I think two to build models of complex multi organ systems or tissue systems. It's really built today to understand in a very deep way.

Cell type of autonomous biological mechanisms and we're working on that we've got spheroid models in Organoid models, both internal recursion and potentially through partnerships that we could be working on in the future that we think will move us in that direction, but if I'm very honest today I don't think for corrosion would be best suited to go after M. S.

Or re myelination, those certainly will be working with our partners at Roche and Genentech to take this platform in whatever direction Theyre most excited to drive it.

We certainly know that there's a high degree of unmet need in that space.

Alright next up we have a question from Stephen Moss, who asks on causal AI modeling with Tempus data will be used for internal drug discovery efforts, our partners or both any change in BD discussions post tempus and any economics, great. So Stephen the economics are both in the deck here and in our filings I'll, let you.

Take a look at those just for the sake of time, because we're almost out of time here, what I do want to say, though it's absolutely we will be driving our causal AI models for our own internal programs as well as for closely partnered programs and recursion. So for example in the context of our oncology collaboration with Bayer or our own.

College collaboration with Roche Genentech, we are able to train models on the temper stated that we deploy for specific programs with those partners. What we're not allowed to do it as a sort of resell the data or memorize the data from Tempus and act as a conduit without a real skin in the game partnership, but when we've got a deep robust partnership like we do with Virginia Tech.

With Bayer we are absolutely allowed to take those learnings and advance important and one thing I'll say you know this year, we acquired two companies. We built exciting New Foundation models, we signed at the Tempus deal and we've been very upfront with our partners that we intend whenever possible to bring all of those updates into our collaborations.

As fast as possible because we are incentivized to drive medicines to patients with our partners.

Alright.

Last couple of questions here here, we are looking at CCM gills, asking what can you guide if any on the upcoming CCM readout Gil all were guiding at this time is that we're gonna have preliminary or.

Or I should say topline safety, tolerability and exploratory efficacy coming out in Q3 and.

And we're excited we hope of course that those data are positive that they lead us to be able to advance that program forward for this important area of unmet need, but we know that regardless of what those data are they're going to help us improve our platform and to learn and grow as a company.

Next up from N K, how is recursion thinking about commercializing at CCM program. If the data is very positive great question NK.

We've got a broad commercialization strategy here at <unk>.

We do believe that some of our early programs. If they are successful could be robust opportunities for us to out license sell or otherwise partner those programs. So that we can bring money back into the company to create a self sustaining platform because unlike many other biopharma companies, who are focused on one or two exciting program.

We believe that on average every program at recursion should be better than the one before it and if we truly believe that we should be willing to sell or license. Our early programs. If they're successful in order to subsidize and pay for the next five the next 10. The next 20 programs that we advance ever kirschman, So CCM could be a good candidate for that.

Now over the intermediate or longer term.

We'll have to see how the rest of the industry moves we've been generally disappointed with the adoption of some of these technology tools until very very recently really until the last 12 to 18 months, where it feels like the industry is finally, starting to get really excited about the potential for ml and AI and so depending on how fast the industry goes we may decide one day.

To actually take our programs forward and commercialize them, but I can tell you. If we do that it is very unlikely we will commercialize those programs. The way. It's done today I think we see lots of opportunities and you're starting to see even larger companies like Lilly doing a direct to pay our direct to consumer kind of play and I could imagine recursion focusing.

On something like a membership model to drive the incentives to physician in favor of the patients and in favor of using all of recursion molecules, but we're really talking about the intermediate to long term there.

Alright.

It looks like the team has done over a final question due to time, if your company wasn't animal what animal would it be this question comes from journey Gray will end on a funny now obviously our company would be in octopus and you can see here when we got our first phase two program, our first patient dose in our first phase two I promised to the comp.

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I would get a tattoo to market that milestone, which we hope will be the first of many.

Octopus plays a very important internal role recursion and I think it's the perfect animal for US. So thank you journey for that funny last question well I hope everybody enjoyed this first earnings learnings call. It recursion, we intend to do this over the coming quarters.

We got a lot of potential milestones in 2024 and beyond so I think these are going to be really exciting I'm going to have other executives join me on future learning calls and if you have suggestions ways. We can make this better we want this to be adaptive we want this to be accessible and so please reach out with that feedback on our social media platforms. Thanks, everybody.

Body for tuning in and I look forward to seeing you again really really soon.

Yeah.

Switching gears Shopify helps you sell smarter every stage of you take full control of your brand with your own custom online store looks.

It looks amazing find more customers with our easy to use marketing to them.

Keith.

And let the best converting shutdowns on the planet can say whatever.

This is the ground chroma shopify, which to shopify today for one dollar amounts trial at Shopify Dotcom fashion Youtube audio Shopify Dotcom slashing Youtube audio.

Yes.

It's one of the world's leading software and technology developer and be a little bit.

Introduces new technical discoveries.

That's amazing and beneficiary.

And around the world.

Make it more individuals that using cloud the Google cloud platform has been used in Chi.

And in recent years.

Although the idea will be cloud services for global consumers.

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Our computing resources.

So on realizing the popularity and the new one.

About computing resources of GCB I have.

Video can be done on Goldman cloud in that get orders Green Green.

In this video I think is true crucial JCB services.

That's correct.

So without doing any further ado, let's get started.

But before diving deep into the concept, even first covered basically starting with understanding what does GCB followed by the GCB took all yet and that should be the next bill GCB services.

And its infrastructure.

Neither clinical medicine between JCB, He got Louis I'm, Asher along with hands on experience with JCB.

They've been more on the ECB that hosting and then even understand Goldman machine learning.

Roper demonstration and that's not even looking to the Sanchez, Google cloud platform fundamentals.

And how exactly that backfire.

And where do you like it.

That's on your GCB related concepts would have been killed.

So for the screening with me.

Specialist together will walk you through the different Shenzhen keynotes.

So let's start.

I think the deal on.

And that's your target frankly too.

Before we begin mixture to subscribe related to China and hit the button and then on this in a data simply don't.

Good morning, and good evening, everyone. So welcome to this session on Google Cloud platform. My name is a J and I'm an instructor with simply and also working as a big data and cloud architect what media clients across the globe in this session, we learn about Google cloud platform.

And then we will learn about what is cloud computing why Google cloud platform. What is G. C. D that is Google cloud platform.

<unk> cloud platform domains of O.

The use case that is federal use case and also a quick demo on using services from Google Cloud platform.

So when we talked about Google Cloud platform. We also need to know what is cloud computing. So cloud computing is the use of hardware and software competence to deliver service to it and network users can access these files and applications.

And services provided by a cloud providers from any device that has access to intimate and short cloud computing is a way manage cloud provider for wage access to different services such as it as sources and when we talk about the sources here, we'd talkable computing we.

About memory, we talk about processing power, we talk about storage and we also talk about different services, which cloud providers make available for the users know that allows us for automatic software integration.

Backup and restoring data unlimited storage capacity, having a reliable use of different services and having a cost efficient solutions.

Now, we know that cloud computing.

Models that are different service models. So you have platform as a service you have infrastructure as a service and you have software as a service and these are the prominent models, which are offered by different cloud providers, such as Google cloud such as Google you have Amazon you have as yours.

And also some organizations build their own private clouds using open source open stack, let's focus more on Google cloud platform and what it has to offer and how it is gaining popularity in the market with different organizations, which would want to work on.

On a cloud platform on which would want to work on and infrastructure, which is modernized. So when we talk about cloud computing. This is one of the approach which organizations can take to instantaneously benefit from modernization using different services.

<unk> platforms and also newer technologies for their various requirements such as scalability our dynamic businesses.

Before we get into Google Cloud platform. It is also good to understand where you can find some useful sources of all Google cloud platform and Kid you have a website, which is from you or Google cloud. So this is cloud Academy Dot Com Slash library, slash, Google and <unk>.

You can create a free account now that would give you just 70 style, but that has lot of videos, which talk about Google cloud platform and essentials and it has media certifications related videos, which you can access and then after seven days. After you have tried you can.

Obviously gulfport it paid account maybe you can dig detailed learning from peers.

Now this is your training library, which shows you a different cloud providers such as Amazon Microsoft.

G C D. And then you have various of the topics which are the specifics.

Organization.

You can obviously look at the pricing and you can also look at resources, which will give you a good <unk>.

Expedience and learning from these videos you can also create if fee account, which I will walk you through later so you can create a free account on cloud at Google Dot Com now when I say free account that basically means it allows you to create an account and every user <unk>.

It's an account gets at $300 secret it which can be used to practice brush up your skills and also explore and learn about different Google cloud products, which are offered by Google.

Now coming back to Google.

<unk> cloud platform, let's understand why Google cloud platform. So we will drill platform is popular for many reasons no. Let us see few of most important reasons why it stands out.

Can you talk about pricing pricing is one of the significant factors that make Google cloud standout among the others cloud for wages.

It offers a monthly pricing plan, which is bid according to monthly usage and when we talk about billing him. The billing can be in ours. It can be minutes and it can also be in seconds. There are different options. When you talk about your pricing, which can be found from you on Google.

Loud webpage now pricing could be.

<unk> on pre Emptive machines, placing could be based on reserved instances of the zone the sources.

I'll show you the link where you can find more details on pricing part of it so Google cloud really has various pricing options, which help customers in their different requirements, whether they would go for it.

If your data is locked up the power of your data is also locked up don't worry about we're here to help and data is the core of all of our businesses. It's the building block of our AI future, but let's face it I only works for you. If your data works for you. So today, we're going to talk through the challenges you are probably facing and.

So you how to bring your data together with Salesforce data cloud from any source and use it to enhance every customer interaction now let's get started.

Yeah without data it doesn't mean anything.

Trust was the most important thing I needed a trusted source to secure my data and data cloud was the perfect product.

Data is really important to unlock AI now we are able to connect all kinds of internal systems to the Einstein one platform.

[noise] Deseret welcome. Thank you. It's so great to have you here. Thank you for having me of course, so it just seems like yesterday the whole conversation around January of AI to fight.

Pop up on a theme, yes, and everybody has been talking about it.

So I would love to learn what are the conversations that youre, having with some of our customers and where are they in this journey you know ankle never forget November 30th 2022, and what's the day that tax equity came out yes, and the reason why is because it's the first time that all of us in the world can actually playing with AI yeah.

And since then every business has been trying to figure out okay. Now that I have the technology, how can he brings us within my own organization right and so it really started out with the conversation of like what F to know how do I bring this into my company right or the.

One thing that's super important, though either it be predictive or generative AI, it's really around the data that you're bringing the quality of that data has to be really really valuable for every company that are out there today Grainger perfect. Yeah. So what we've been seeing is this big transformation, where they want to take advantage of the data, but more importantly that they can actually do something with the data that's the unlock right now.

Well, we've heard a lot about AI, yes, we know that AI and data go together, yes data has now become very huge topic. It is this buzzword in I think a lot of people are really excited about it so they're also like ocean.

So what is the focus of band for enterprise and how our companies thinking about data today, what challenges are they facing as it pertained to the concept of data yeah, a lot of enterprises right now because theres. So many different data sources, if emerging on a constant basis and so what they're really trying to figure out is like what are they how do they actually organ.

Is this how do they actually do something with it right and.

That's an interesting stat from Salesforce I think it's 51% of I T learners don't have a unified data strategy. That's scary that is very scary. So you can only imagine like this is just the beginning and I wanted to figure out okay, now that they've harnessed it with them within their leg view, what do they do with it how do they actually take advantage of that how can you trust. It how can they make sure I'm taking action on that data. That's the next step from us.

Enterprises well the topic of today is is your data locked at.

This notion of like locked up is it trapped tell us more about this concept of trapped data and why we just need to be aware of that and how we get Pasadena locked and trap [laughter]. Yeah, a lot of it's because a lot of data is in silos or in different like data links our warehouses, but it also ties to like governance concerns or it could be that they don't want a copy data.

One place to another so people are trying to figure out okay. How do I have a centralized unified view of all the thing that lives in different places within my organization, whether they can actually do something with it and that it's secure right. Yes security is a much a very important factor for all of US of course, so how are companies coming to Salesforce for help now do we have the answer.

Do have telecom on the big answer around data cloud and I think that's what's the signed lock with everybody today. So number one a lot of companies have already invested as I said in other systems data lakes warehouses telemetry data right website data you name it basically and the whole idea of a sad data cloud can now bring a unified view with our customer profile and when you see.

That view you can do something with that information right, so either you're saying Oh and I loved the examples like on the marketer right and you know I go to a website and looking at perusing a bunch of stuff and then I also wanted to see what my history of what I've purchase lifting up before I imagine that you can now send an automatic email, saying hey here is a 10% coupon I know you like.

Right. You know makeup in this color or whenever it right right. It's as it tells the story does tell a story and so the whole idea of this is I think it does this with a very seamless way of doing it without having to go look at all these different databases for this information to like bring it all together what we've heard so much about data we know that Salesforce is not the first company to ever talk about.

Right, but I'm curious now as it pertains to data cloud in particular, how is it different from other approaches that are out there in the market. The thing that I think is one of the smartest things that salesforce stead from like 25 years ago. When they first started the company. They invested in meta data and people got whatever Medicaid I know we've heard so much about me.

So that data on data I know that sounds like a crazy concept that the idea of it is that like went let's say contactless as an example, right there's different layers of contact with the name as the last name. So the address what's your email whatever it may be right and so it's just making sure that everyone has an understanding of what that layers are that you can actually pull that when you're actually asking.

<unk> are you that it has context the information that you are basically looking for right and really tells again that whole story of who the customer is that's right well this isn't such a great conversation Deseret. Thank you so much for joining us and they're not going anywhere I'm not going out.

Around because we have a live Q&A coming out very sound.

Well next up let's see data cloud in action with a demo led by my colleague Stefan Chandler Garcia, who has some really exciting features to share with us.

Thank you Sheila I'm Stefan Chandler Garcia, the director of strategic content for audience relations here at Salesforce I'd like to double click on some of the amazing innovation that's possible. Thanks to data cloud, we're going to start out here within Salesforce. This contact record has lots of rich information about our customer as well as all of the CRM relay.

Data like their activities cases and opportunities as.

While this is very powerful for interacting and tracking engagements with your customers. This isn't truly a complete view of our customer most businesses have vast amounts of trapped data throughout their application landscape and I want you to think about how that trap data could help you close cases faster and create a better experience for your customers.

With data cloud you can integrate and harmonize data from all over your enterprise you can create data streams that are used to connect data from anywhere whether that be salesforce applications like marketing cloud and commerce. Many other data sources like AWS, Adobe, Amazon and snowflake or using one.

Of our hundreds of connectors with meals at.

All of this data can be used throughout Einstein, one and powered by the metadata framework that you know and love So let's check out that contact record. After you use data cloud what youre seeing here is the full picture of your customers' interactions all within the sales force you why this is possible thanks to our robust metadata.

Framework that powers your customer experiences.

CRM enrichment allows you to copy fields directly from your insights and data cloud and the new data cloud related lists enable you to bring all of this data right into the flow of work. So check out. This list of reservation data. This data is coming through directly from Snowflake you can bring your own lake to data cloud from Snowflake.

Through our zero copy connector now over in Snowflake, you can see the reservations here in the worksheet, we can query them and we can even modify them.

Now when we go back over to sales force, we can hit refresh on the component and look our changes were updated in real time. This means that you can take action immediately as your customers need it okay.

Okay, one more thing take a look at this prediction here with data cloud, we're able to create predictive insights about our customers. In this case, we want to predict what the propensity to buyers for this customer you can create these valuable insights and no time with model builder.

So here inside of model builder, you can bring your own predictive or even generative AI models directly into David loud, so let's create a new model.

Here you can connect an existing model from one of our partners using Amazon Pagemaker, Google cloud vertex or data bricks, but even cooler you can now build your own with the low code model builder. This means that you can create predictive AI models on any of the data that you have in data cloud here's that propensity to buy model that we shared a moment ago.

You can see that this model has a 91% accuracy rate for predicting the booking outcome. It uses data about the customers' engagements to show US how you can see here that when a customer is traveling with their family. There are much more likely to book. This is all great data to present and analyze but I want to show you. How you can activate it so let's head.

Over to flow data cloud is integrated with Einstein. One. This means that you can use a prediction to trigger a flow.

This flow even uses AI to help us generate a personalized SMS, let's check it out.

We've been able to just trigger this SMS.

We just don't what happens when you bring data together with predictive AI to help us connect with our customers at the right time and with hyper personalized content all thanks to the power of data cloud and Einstein one.

You just saw how data cloud starts out by connecting data from anywhere into your CRM apps. Your data lakes. Your warehouses all of that data has been harmonized and mapped to a common data model that creates a single view of the customer you.

Can treat this unified profile like any other sales force object with the vector database built in you can also bring in unstructured data to data cloud think E mails social media replies knowledge articles cases. These can all be indexed in search. So that you have an even richer view of your customer wants all of that data is ingested.

It you can activate that data across any CRM app. This means real time insights real time actions real time automation and even real time personalization now you can see why our customers are so excited about data cloud and the Einstein one platform.

Our product and engineering teams have been hard at work to bring this to life. So let me walk you through some of our latest innovations.

Data spaces is now G E with data spaces customers can logically segregate data metadata and processes for departmental regulatory and compliance needs.

Mato builder is now G. A customers can choose an L O M or build an AI model based on the job to be done.

Model builder is a no code low code and pro code way for companies to build their Unpredicted AI models trained on their data cloud data for generative AI model builder allows customers to select from Llm's managed by Salesforce or bring their own models businesses can also use predictive and generative and models from Salesforce partners, including.

Amazon bedrock, Amazon Sage maker Anthropic co here data bricks, Google clouds, vertex AI and open AI and even trained select predictive models on data cloud data without moving or copying data.

With data plan triggered flows customers can automate business processes based on a change in a data point from all across data cloud data sources or when calculated insights conditions have been met now users can test and troubleshoot their flows before activating.

The Snowflake connector for data cloud is now G E.

With Snowflake, we can now connect directly to our data streams within data cloud. This allows us to take all of the data within our snowflake data warehouse and visualize it inside of data time with our zero copied zero E tail connector here.

Here, you can use and modify this data anywhere in your tech landscape and all of the changes are replicated right inside of Salesforce and data cloud.

And don't just take our word for it let's hear from Heathrow airport's platform architect Spiro Black surplus he sat down with sales forces AVP of data cloud James Bishop to share how data cloud has revolutionize their business.

Yeah.

Welcome to spirit from Heathrow that would.

A great example, you've taken the time to join US a nice very straight from voluntary okay. Thanks, so much for joining us. Thank you for having me tell us a little bit about some about about Heathrow and a value game and the language you operate.

Women.

Some additional relief on the architect C. As you can imagine Heathrow is very wide and varied.

Business, we have 80 million passengers a year on average 70000 people come to work every single day.

So my team is responsible for the commercial revenue and the passenger facing digital activity.

80 million passengers will wow, so as a longtime marketing cloud customer and we've done our business together in the past you'd originally invested in the marketing cloud and then moved on to make an additional investment with the Chancellor is new data come out for us.

Wonder if you could tell us a little bit a little bit about the reasons behind that that investment and the problems that you were trying to solve.

I agree with Ben.

Marketing cloud implementation.

With today's cabinet Big <unk> sales force, we saw massive food franchise.

An increase in digital revenue, three personalization and with todays clouds.

Enhancements for real time be reasons to take it to the next level so billions of.

On the days that we had already collected in the classical marketing from our ecommerce channels, our wood panels and.

And our Wi Fi, though gains and we've seen a 30% uplift using personalization with that data center to wherever you see what the data cloud.

Enable has stayed where it was very excited about it is to bring in all of our real time streaming operational data to further.

People, who are actually would repeat for it.

If you think about buying some of our reserve and collect service currently we only know the data from your reservation and.

So we never once time in flight is going very well.

In today's cloud, we can bring in our operational by you. Sir we will now feel quite as being delayed if you fly has been cancelled won't perform space food view too.

The latest version and then we can enhance the messaging we can say to you.

Furthermore, we know you'll fly it brings laser pocket proves to be waiting for you.

<unk>.

We can see your influence be counseled would you like to reschedule. So it's building on top of standard marketing.

Capability that we already have very good success for him to further enhance customer experience pretty firm people, maybe a little bit surprised here that you don't have access to all of the data those passengers.

Before they before they joined the Apple.

St data I think is probably going to be particularly important to you in the commercial operations at <unk>.

100%, so as you say.

A lot of people are surprised but we don't.

We don't have access to two to that customer data actually the customer resolved constantly airlines predominantly so we really have to work very hard to have a value add propositions.

In order to have an exchange with customers who they were.

Willing to give us a very too soon.

Our loyalty programs.

Wifi.

Parking e-commerce platforms.

This is how we interact with our customers when we get information about our customers when they're coming through we had pool and that enables us to bring all that data together for ADP, bringing the connect comes from outside of samples as well too to form a customer 360 and create personalized experiences next basketball things and really.

Creating engaging experience for people who come into the airport.

And when you talk about enhancing their journey through the excellent I mean effectively.

Because it's a very short window of time that they are in the ankle is spending less time on so.

Some of the more routine activities and trying to help them understand where they are going to be how they can improve the shopping experience spend more time in the shops and restaurants whenever it may be is that the plan.

Zeus is and it's bringing the operational data insight.

Ample committed confusing places there's lots of information people are interested.

Where do I go to chicken.

Just curious what the securities accuse homes.

More information that we can give to passengers to help them in this value exchange, you'll give us more information.

Through the airport.

Will allow the customer to have more time to engage with our other.

Experience is going to be able to be.

Darling.

And reason.

Well it does actually mean in the end for the business in terms of the.

Okay P honestly.

We've obviously.

It will bring more people have dwell time call it in in the.

Retail space the more we can engage with them.

And give the customers.

Curious who to office hopefully they'll spend more money.

So yes, we very much see dates clouds, the personalization and the real time capabilities and the key to being able to unlock our engagement with the passenger.

And he tried to sort of the net promoter score so I've got them of their experience and obviously there is a commercial element to essentially spending more in the dining and retail experiences that feature.

So that they are experiencing.

And the Apple yesterday, and actually one of the big benefits from that initial program is where we find that if a customers interact with us on a digital basis and they have a much better.

Constant since Frac crew school, then if they don't it.

Interact with the physical elements of that book, if you will.

Say that digital experience really does improve for customers.

Experiences that Heathrow, yeah, yeah, Okay. That's factoring in in terms of what most next and what you're planning and how you can maybe exploring more of a technology indicators, but as you know most of them. The most in the roadmap.

So the new exciting beginning tomorrow as everybody probably knows he's around AI and what opportunities do we have to.

Exploring.

Bringing efficiencies both to the operation.

So a better experience for our customers.

We are.

Very shortly gains be introducing service GPT, but.

That will help at all <unk>.

<unk> sensor Asians.

Really we have the cat program. These interaction channels so in phase one be using.

Two on.

I understand what the customers are asking for.

Responses initially brackets and service agent and then back to the customer based on the knowledge base that we have in samples of major cloud to power AI era.

Susan.

Well.

I want to thank you once again for joining us thanks for giving US your time. This afternoon and look forward to doing most of the other data content. Thank you very much been a pleasure that people want to just thank you.

Our many thanks to sparrow for sharing Heathrow story, 30% increase and first party data capture how impressive is that.

Are you ready to get started with data cloud our professional services team is here to help you with more than 1000 certified data cloud consultants globally and over 200 implementations and worldwide. They can help you with everything from developing a strategy and roadmap to deploying the full power of data cloud across your entire business.

Visit the link on your screen to learn more and now it's time to hear from you. The moment, we've been waiting for we're here alive and studio to answer all of your data cloud questions and our moderators are standing by in the chat. So let's dive in this is great let's get to that alright, alright. So we have our first question that's come in and this is like.

Good one I think this is a question that a lot of our viewers who want the answered him what's the fastest way to get data cloud activated in my account great for existing customers. If you've got a salesforce dot com flash data is actually like a button that says how to get started right away, which is often we make it easy to make it very easy and not only that but we actually gave you some credits to kind of get started okay.

Through the process such as awesome now for net new customers you can click on Assia expert on the website as Matteo and will contact you and get her fans I love that okay. So we have more questions that are coming and here's our next one is there a limit to how much historical data I can I can apply to data cloud Manhattan gets a great yeah, that's right.

And now I mean, if you think about all the customers that we have I mean from financial services to manufacturing everyone has a ton of data and now Theres no limit here I think the big thing we want to make sure of is that you want to start with a small project and then start to implement even bigger ones. As you go along by absolutely Yeah, No. We absolutely element, Okay I love that so we it's easy to get started and easy to get in that element.

Okay, We love it we love it Okay. So let's talk about how long, it's going to take to get data cloud and finally, we actually have a couple of questions that are duplicative asked that a lot of folks on and now yeah I would say it really depends on what you're trying to do we have things from small projects, where you're just trying to get women engagement data to your customer profile or you can have something that's a complete data transformations, where youre really trial.

Enable AI across their entire organization. My biggest thing is that we have as you mentioned earlier professional services that can really help you get started we even have packages that are just like that they're getting started packages to like the more comprehensive staff. So it really just depends on what youre trying to do okay. So it depends on what you were trying to do depends on how much data you have that's right where that yes. How early you are in the data.

Yes, a lot of things exactly so here's another question from one of our viewers, we have an existing snowflake deployment.

There's a lot of our data currently do I need a copy of all of that data to sell forth. So this is the best part Snowflake data breaks. The list goes on we have lots of partners. We are partners with these folks and now we have something called zero copy exact this is really where you get to virtualize that data so you're not copying getting them from one place to another a lot of ITG folks really like this because it makes the implementation really easy.

So no absolutely not this makes it really simple so okay. Yeah. That's great. Okay. So it sounds like we have a lot of solutions that are already teed up. It's just a matter of getting started and I think most importantly, we talked about this earlier and seeing the value of why data is so important now totally and what opportunities. We have ahead as we think about AI, yeah, and I think the biggest thing in fact.

Zero copy part of it is that once this now in your full view within Salesforce you can take action on that data, even though you've invested with <unk> with snowflake or any of the other.

Data lakes out there now he can actually do something within the Salesforce environment, 100%. Okay. We have one more question. That's come in this is a good one because I think oftentimes whenever we think about innovation technology implementation, we think about resources yeah. How much is it going to cost me and how many people do I need to put on staff. So my question is how many people do I.

Need to have on staff to implement data cloud.

Christie, we well first of all we have such amazing resources at our disposal with trailhead and if anyone has not taken advantage of this please do it's one of the best resources I've seen out there and you can get really started with data cloud a S. A P and like learn how it all works, but for all of our existing admins and developers. They can actually go in there and learn how it because it's already been native onto the ISI one platform they can get access.

To this end learn how it works for them. So that's like the number one thing I would say so it's becoming a trailblazer. They kind of had outweighs that learning getting educated and I think it really does tie back to how much data are we talking that's right. What's the scope of the project are we trying to bite off more than we can chew or are we really going to phase this outright and really utilizing many of the free app.

Available resources that Salesforce has so that trailblazers can get to work to solve the solution exactly yeah, yeah to learn more about data cloud click the link on screen. Thank you so much for watching and we'll see you next time.

Do you have the service models, such as infrastructure as a service platform as a service or even software as a service one.

One more attractive thing about Google cloud pricing is that it provides committed use discounts for.

For example, under this scheme you can put Jesus specific among them virtual CPU cores and memory for up to 57% discount off regular prices. If you commit usage. What I then why don't see now. This is just one option that our media such options, which you can learn about from the Google cloud speeds.

And that really suits different customers for their different requirements.

We talk about speed, we don't need to really challenge. This aspect when it comes to Google services. So we will provide you with Google cloud and Google App customers speed up to 10 terabytes because of its fastest cable system.

<unk> kind of machines, which can be used if you're talking about computations.

Talking about memory hungry applications or even storage intensive workloads all in all speed is one of the defining characteristics of Google cloud services the.

The cable has connections or the U S with schools.

Main cities in Japan, and even major hubs in Asia. This speed enhancers performances and leads to customer satisfaction now when we talk about customers. Amy on every customer would prefer to have low latency high throughput based services they would want to use higher speed.

<unk> to process their data in as less time as possibly Google provides a low latency network infrastructures. In fact, you can say that when you are using Google Cloud services you are using the services from the same infrastructure, which Google uses for it.

Popular services, such as Google search or even Youtube, which is one of the second largest repository, which can be accessed for videos no. When we talk about big data Big data is data, which is very complex has lot of other characteristics such as yours.

<unk> you have been and also be you have <unk> you have it.

It's time to Unthink, what business banking can be blue line is uncomplicated and nickel and dime and built uniquely for Ya plus you never have to step foot in a bank again.

Move on.

D valued the volatility whether it would be and so on so if an organization is working on big data, Google cloud can be a better choice because Google has many innovative tools for cloud with housing for example, such as big gritty and even real time data processing.

Tools, such as data flow Big Weighty is it did have a dose that allows massive processing of data at high speeds basically work in one of your structured data. Google also has launched some new machine learning from artificial intelligence tools now there are various other services, which begin Mitch.

We can use from Google cloud platform, but let's understand what does Google cloud platform and what are some of the services.

Even the services, which are not listed here can be found in.

Console from what will come out so what is what will cloud platform GCB. It as a set of cloud computing services for weighted by Google that runs on the same infrastructure as I mentioned that Google users and user products like Youtube Gmail, and even Google search the video set of services.

My Google Cloud platform.

So you have services with jobs specific to computing requirements and again in computing you have various different options available you'll have machines, which are <unk>.

Butte optimized machines, which are memory optimized machines, which are storage optimized and also we have certain machines, such as premium des which basically means that you could get a machine to work on that if far lesser price than any of the machine but.

When we talk about pre Emptive. These are the machines, which can be sequestered on demand and they can be taken back by Google at anytime you have networking related services, which can be very useful when you are setting up your own applications or youre services across the globe.

Also have different services, which are specific to machine learning and organizations, which would be interested in working on machine learning artificial intelligence would be really interested in using these services.

Also you know would give solutions to work on big data and Big data related technologies now when we talk about Google cloud platform domains. So weekend breakdown. These services into specifics such as Youll have compute now the compute service allows for computing and host.

<unk>.

No.

You talk about computing here there are different services such as App engine you have compute engine you have kubernetes, you'll have cloud functions and cloud.

When you talk about storage and database so the storage and database service allows application to stored media files backups.

I like objects.

Services under this are as follows you have cloud storage cloud SQL cloud Big table for unstructured data you have cloud Spanners cloud data stores, but system disc and cloud memory story, when we talk about networking the networking service allows us to load balance.

Across the sources.

I mentioned earlier the resources could be your different sources.

You would be using from a cloud platforms, such as <unk> devices, you had instances mamady optimized on CPO optimize instances or other resources, creating D anesthetic gods and much more so media services under this BBC that central watch will play with cloud you'll.

Cloud load balancing cloud armour cloud C. D. M. You have cloud interconnect BNS and network service to yes, when we talk about the Big data service. This allows us to process them quite a big data in cloud No media services. Under these are as follows you have big equity cloud cloud.

Data broke.

<unk> cloud data lab data prep cloud hubs hub, which is published in subscribing system, you'll have cloud data studio.

Also have the developer tools and develop a tool service includes tools related to development of an application no media services. Under these are as follows that is you'll have cloud SDK software development kit you have deployment manages cloud source as opposed to trees and cloud does slip.

When we talk about item to be in security, which is one of the primary concerns for any organization any user who would be interested in using a cloud platform. Google cloud really has taken care of this so when you talk about identity and security domain. This deals with security services.

Bell media services here.

Cloud identity, you'll have identity and access management that as cloud E. M. You have item to be aware proxies you have cloud data loss prevention API security key enforcement key management service and many more.

You also have cloud services, which are related to internet of things. So very much would be used by organizations, who would be working on the Iot devices. All of the data generated by these devices. So media services here.

Cloud Iot God, you have H D Bu and cloud Iot also when we talk about cloud that is artificial intelligence. This comprises of services related to machine learning and much more so you have cloud auto ml cloud GPU cloud machine learning engine job disk.

Because of the dialogue slow enterprise natural language cloud text to speech and much more.

If you would be interested in services related to API platform and data.

Services under this category or this domain. So you have maps platform you have APG API platform monetization.

Global portal and analytics that is EPA and analytics, <unk> Sens cloud data points and service infrastructure.

So these are some of the listing of services under each cloud platform domain.

Now, let's look at federal use case, and let's also understand what was done him. So federal is one of the famous chocolate and ranks third among worldwide chocolate and confectionery producers. It was found in 1946 in Italy.

I'm sure you would have seen the federal chocolates spin you would have gone out to license. Okay. So the challenges here was that federal as we know is sold in every supermarket and is known for its quality once the business grew some issues.

Right and that's what happens when the business grows you have issues popping up which could be related to the volume of data the speed with just a few days getting generated the variety of data and also looking at your platforms with simple different dynamic applications audio scalability requirements.

Performance requirements and so on so it needed data storage processing and analysis system far and wide customer database.

There was a huge gap between the company and the people who bought it schools, but theres a company the light on data given sales outlets now that's one of the challenge federal wanted to create a digital ecosystem.

As a point of contact with its customers and also a foundation for an innovative data driven marketing strategy. What was the solution here. So one of the service of cloud platform. So the Google Cloud platform is big Lady and this was an answer to federal's challenges since it was capable.

Manav hyper fast and efficient data analysis as the solution now as I mentioned Big Brady is a data warehouse, which allows you to store structured data now this could definitely be used as a service where you could store in any amount of data and you would not be.

B four stood at so there are different pricing models and for data up to one petabytes you would not be charged anything and if you would be accessing the data that is reading the data at processing the data from the equity that's where the pricing model kicks in.

Now using Google clouds, Big gritty business analysts of federal when able to store and analyze massive data sets and a very reliable fast and affordable manners.

Consumer behavior and sales spectrum data reports, what easy to build and automate.

And the analysis also followed federal to adult.

Advertising across radius marketing channels to serve the customer needs in a better way what was the result.

They could be wide their database into real time actionable consumers clusters to generate more accurate user profiles.

Hmm.

Hum.

So I don't know was also able to personalize its marketing strategies to match the user needs now.

<unk> cloud platform completely de load the website mobile content and advertising and created a very cost effective media buy strategy.

These were some basics on Google cloud no as I said, you can always find a lot of details about pricing about services and also all the services and access as well and your free trial account now let me just walk you through here. So one is you can always find details on.

Acumen nation on each of these services. So if I would click on getting started you have quick start which basically shows you a shot.

<unk> you have trainings and you have also certifications now if we click on quick start now that.

That takes me to this speech, which shows me.

Quick starts or if you would want to understand about different projects. If you would want to look into the documentation on creating a linux switch on machine of storing and file and shading. It deploying it containers docker container image and so on so you have a lot of weak starts here you can also look at cloud minute.

On the same page on the right side, you have docks and once you click on that it takes you to these links now. This shows you based solutions, which shows you. The top use cases best practices. All the solutions. It's always good to learn from these use cases, which are available and she is you have different <unk>.

Featured products and all the different services, which bullish loan office now you can click on featured products and that basically shows you a list of these products now. She is we have all the solutions, which you can look at architecture database enterprise level Big data and analysis gaming.

Internet of things and so on now if you go down here. It shows you featured products and that shows you some of the important products such as compute engine, which is belonging to the compute domain you have cloud drum, you'll have and Poe switches for migration and basically cloud adoption.

When organizations would want to move from on premise to cloud based solutions you are visually I, you'll have cloud storage now that basically allows you to store any kind of data. That's an object. So this was acting as an object storage you have cloud SQL, which is basically a ready to use service.

Whether you would be using my SQL post glance on any of the skill set of our database services you have big query, which is a data warehouse, which basically allows you to store your structured data and then you have your AI and machine learning related products. So you have auto Ameren, Missouri I video extra speeds.

Speech to text and so on now you also have different platform accelerators, which can be used and in any of these cases for example, if I will click on compute engine.

As a featured product from Google Cloud that shows you basically youre quick starts using your Linux machines, how to guides, which tells you completely working on B M and stands on working on storage working on persistent days and so on and it shows you the documentation.

Now you also have products and pricing options would you can see for GCB pricing and you put straight away go to the pricing you will be looking at solutions, which talks about infrastructure modernization. Now this is something which organizations are interested in when they would want to move from there on.

On premise solution to Ewen, who will cloud no shift.

Say for example, I click on infrastructure modernization you can always find some case studies what are those different solutions you be half year right. When you took over Google cloud. So you can always slip on C solutions and you can look at we have migration.

<unk> cloud right.

We'll cloud what it can be used for the embedded in service.

H B C that is high performance computing and about the services. We were alone in detail later now I can go back on the same beach.

I was clicking on the services. So you have quick starts you have how to guides you have deep understanding of different concepts right and he is if I click on on how to guides. So that shows me one other different ways and you have that you can work with compute engine and working with different instances, although it.

White exhaust of content, but then if you follow steady D. Then you can learn a lot about cloud.

Now here I can just go back. So this is just giving you some idea on the different products, which are available from Google cloud looking into different sections, finding right documentation here right and you can always look into each one of these in detail for each product, which.

Google Cloud office.

No if we scroll down you could see all the options on all of the domains, which we see here right. Let me just go back because we got into solutions now we have quick starts right.

And then you can basically strong all the way down to look at cloud SDK, which can be set up on your windows machine like I have set it up on my Windows machine plus when you use Google Ponsolle you have E. G U E, which I'll show you in a couple of minutes and also a cloud shell made you can use.

U N command line options to work with Google Cloud platform, you have cloud console, which is nothing what youre.

Taxes, you had of sources now you can also look at in depth tutorials pricing and you have different other options. So let's just click on pricing here and then we have your price list, which basically gives you details of different services, what are available and what other services.

So on what are the prices. So for example, if I click on compute engine right and this shows me the pricing aspect of compute engine, which belongs to compute domain and here you can see you have VM instance, by saying you'll have networking pricing sold tenant nodes, which are specific to particular organizations.

RDF organizations would want to have dedicated nodes you have GPU based pricing.

General processing units you are missing any major pricing and you can click on any of the links and you can see pricing, which also shows you defer.

A different kind of machine types of share you have different kind of machine types. It says and one and two and two B E.

You have memory you optimize machine types you have compute optimized you have premium images and you can basically look at all the categories. You can look at displacing which in malls you are persistent displacing for ssds on S. G. DS what are the kind of your majors what are the different network services right.

You can always look at U S machine types, you can choose a particular region right that is the concept of region and availability zones here in Nashville region, you could look at the prices you.

You can also look at standard prices you could be looking for what are the <unk> machines. If I'm specifically looking for VM instance, pricing I can click on this one and that takes me to the VM instance, pricing what is the billing module what is it.

For instance, uptime what is it a source based pricing and then you can also look at different kinds of discounts such a sustained use discounts committed use discounts discounts dropped three M table VM instances and so on and this is how you can be looking at pricing for all different sources and you can choose the resource.

But you are interested in and then look for the pricing benchmarks.

Ovations, if you would want to use and you would want to benefit if you would want to look at what are the core doesn't limits and so on so please explored this link and you can find a lot of information when it comes to technical options looking at different Google cloud products, which we briefly discussed when it comes to domains and what each product.

It can be used for you can always come back to the main page and I am showing you different services, we went into pricing straight away right. Now you can always come back to this page on your cloud Dod Google Dot Com and then you have your solutions.

Which talks about different products right and you can click on these order you can look into technical documentation. So this talks about your different featured solutions infrastructure solutions, you have data center migration related and so on so I could be talking more and more about Google cloud platform. It's a ocean.

It's a huge chunk of different services for different organizational requirements. So look into this link and also what you can do is create a account on G. Maybe I mean, you could create a free account audio could go to cloud at Google Dot Com and then you could create a free.

Like I have created here and I can just click on console. That's my <unk> on Google Cloud console, which basically allows me to work with who will float.

Lot of options here by default.

Create an account now in my case on the top it says it's a free trial account I have $300 credit and out of that to $51 left and 237 days Arnaud for ear odd left for my account now here I can basically see the project right.

And by default, we can create and project are by default. It is a project existing fund any use it. So when you login. It creates a particular project and you could create a new project and project would be to dedicate your different services, so different resources, but a different project. So this is your dashboard.

Which basically shows you a project, which shows you a project numbers and it project I D, which is always unique now you can click on this and this if you would want you can hide this information you can also look into the documentation part of it then it shows you the different services or a graphical inflammation of service.

So what you might have used in past. So have you have compute engine, which shows you how much percentage of CPU was used you can always go to compute engine as a service you can look at your Google Road platform status and look at the World Class status. Now. This is billing now since this shows me the billing period for.

April month, and I can always looked at detail charges I can look at reporting I can look at different AP ice, which Google office and for your different kind of work you can always go to the EPA overview or the API link and enable or disable any API once you enable or disable.

Any API then you can use that so you have new section you'll have documentation and you have getting started guides, which tells you how to work on enable different dpi's. If you would want to deploy a pre built solution.

Dynamic logging monitoring it is deploying a hello world App and so on now this is your dashboard I can click on activity and that would show me what kind of activity I would have done in my cloud platform I can always choose the kind of activities by saying activity type.

Those that are sources now it shows me that I created some VM instances I deleted them.

<unk> updated some metadata I basically worked on instances I changed some firewall rules here I have them also worked on some of the services or the EPA. So I created some buckets, which is flawed object storage and again I have been working with some in instances in trading.

Some firewall rules here I have granted some promotions I am sitting up at policy right. So this activity gives me a history of what things I have done over the past couple of months, while working on Google Cloud platform now.

On the top left corner you have the Hamburger menu, which you can click on this so that's your navigation menu and when you click on this this shows you home. It takes you to the marketplace. It specifically it takes you to the billing you can always look at Api's and services. So shared in API services I can pretty much.

We'll do a dashboard and I can see a report on the traffic on Anna's or latency and the kind of Api's, which are available. So you have compute engine API you have big <unk> query data transfer API storage cloud data broke so these are some of the API as a services I've used it.

Bust might be avon's evaluating your product might be I was using a particular product. So you have.

You would want to use a particular service from Google Cloud platform, you would be enabling visa Apis. So should we see some apis for data product you have logging wanted training you have resource manager at API, you have cloud SQL, which allows you to directly use my SQL post glass you have cloud story.

Age right and so many apa's. So if I would want to enable a particular API. We're just not right now required but then it's good to know you can always click on this one you can search for an API. So for example, if I would say data broke right and that shows me the cloud data broke a P. IV. It's manages hadoop based cluster.

And jobs on Google cloud platforms, like and spin up a hadoop cluster and I can start running some jobs on a hadoop cluster on demand and as I am done with it.

Get rid of it. So this is an API, which I would have to enable and then for every particular service. You also have an Ed many of which you would enable.

Now going back so coming out from this API library I got into EMEA and services now that is you can look into the library you can look at credentials and you can also look at different time intervals for which you're going to see the usage of your different dba's.

Now going back to the menu you have support.

And you can always at each outlook, we will support them. If you are using a bead Watson even with free trial, you can try to reach out and you will find someone to help but it is always good when you have a billing cycle, reaching out the customer care you have identity access management and admin and this is required when you're working with.

Different API as a services when you would want to have relevant axis. You have getting started you have security related options you have and post which is mainly for migration. Now. She is you can look at your different domains such as much of a discount. So you have compute and in Colombia, you have different <unk>.

So you have App engine, you have compute engine kubernetes, you have cloud functions and cloud of them you can look into the storage aspect, which shows you bid table, which is usually and mainly for the unstructured data.

Big table, which is something which gave rise to Pablo noise databases like H base in Cassandra, you'll have data stored which can be used you have firestone file store storage, which can be used to put in data of any kinds you have SQL for structured data.

Have standards as a service you have memory store and you have data transport.

Now when it comes to the networking domain you have all these services such as BBC virtual private network network related services for your load balancing for using a cloud based D. N a S R.

<unk> defined network you have hybrid connectivity network service. He is security and intelligence. Then you have other options for you on operations, which can be used and here you have different tools, which can be used such as cloud build you have cloud tasks container or is it just street deployment manager.

And many more big data specific you have the services here. So you have data broke which can be used to spin up your clusters. You have published subscribed messaging systems, such as now Kafka, which you might have heard of orange needs. Its idea from here. So it bumps up you'll have data flow you have Iot core big equity.

Is it do you know where those package or data or at housing solution from Google Cloud. Now then you have your artificial intelligence related services and other global solutions. So you can always find a huge list of services or you can say at high level solutions offered by Google Cloud and we can use these.

<unk> to basically test some of the solutions use them and work on them. So I'll give you a quick demo on different services, which can be used here from your Google Cloud platform. Now. This is your Google cloud platform and this is your console now. This also gives you a clue.

<unk>, which can be activated so that you can work from command line and you can always find a lot of documentation on that so you can also set up your cloud shell that is SDK on your Windows machine and if you have set it up and then basically you could be doing something like G cloud right.

If the cloud has been set up so in my case I had set up the cloud SDK and basically I can get into that.

Looking at what Bart I have setup, so cloud as D. G. And then I can be using it from my Windows machine asked but my comfort I can also activate the cloud shell here, which will basically open up our terminal and at any point of time you can open up this one cloud children a different window. It is preparing the clarkson.

It will by default set up your project it will set up the metadata and now I am logged into my Google Cloud account from come online and here I can basically use G cloud right and that basically shows you the different options, which are available, which you can be using so if you would want to use.

A particular service you can also try giving you hell and that shows you how do you manage it Google cloud platform right. So you have different options for billing to work with you or different services and basically you could be working on we will cloud using this cloud shell from Europe come online you.

Usually for people, who are learning workload in the beginning it is always good to go for console and use your different services from chip in an easier way, but as I said you can always use that come online for example, if I would just go ahead and type G cloud create in census.

And that will directly take me to cloud console documentation, which shows you the different options, which you can use toward with instances. So I could do a G cloud compute instances create and then I can give my instance name and so on and I'll show you. Some examples on that so you can be using your cloud console right.

That is your cloud shell and you can straight away to start looking from come online Hollywood I would suggest using console in the beginning and when Youre well Expedience. Then you can start using cloud shell to do things from come online and when you're fully experienced you can always switch and certain things are usually useful easier.

Dunn from come online and some are easier when you do it from a console and you can use any one of these options now we can go to Google cloud console now as of now I can close this one I still have my couch. It open if I would want to look at it I can click on this one and I can straight away go to compute engine.

I would want to work on creating some instances onboard cloud platform using the compute engine service and then basically connecting to those instances and basically trying out some basic things. So you can click on VM instances and then once this comes up I can always.

Create some instances as long as you see here I have some instances already created right and I can kind of degree of working on those I can create new instances I can use different options, while creating instances and I'll show you that in a demo in quick seconds. So let's have a quick demo on setting up GCB.

Be instances now before that let's have a quick recap. So when you talk about instance, or a virtual machine. It is hosted on Google's infrastructure and you can create your Google Cloud instance, using the Google cloud console that is by clicking on this and then.

To compute engine and clicking on VM instances no. You can also do that from Google cloud come online tools that is cloud shell and you can be doing that using compute engine API.

So computer engine instances can run the public images for Linux or Windows servers that Google provides you also have the option of creating or they're using custom. He may just that you can create an import from your existing systems you can deploy docker containers, which are automatically launched an instance.

Containers optimized or is now when you talk about instances in projects always remember that in each instance belongs to way Google Cloud console project and a project can have one or more instances.

When you talk about the instances in storage options. Each instance has is small boot, but system disk, which I will show you in for the screens that contains the oils you can add more storage space if needed.

And when you talk about any instances in network. It project can have up to five V. P. C networks. So we be seeing it work is what will drive it. It works and you can watch all private cloud networks, where you can have your resources within your own sub Nick.

In each instance belongs to one V. P. C network now instances and Siem network can communicate with each other to local area network protocol and Insulins use the internet to communicate within any machine so that could be watchful physical what ill tell you its own network.

When you talk about instances and convey minutes you should remember that compute engine instances support declarative methods for launching your application using containers now you can create a b M instance, on a human since template you can provide a doctor image and launch the configuration.

So there are different ways in which you can create these instances and once you create these instances you can say for example, you are creating a Linux instance, you can associate SSH keys with your Google account audio G suite account and then manage your edman non had been accessed or the instance, using I am.

Rules, if you connect to you an instance, using G cloud honest dosage from console, which we will see later compute engine can generate SSH keys for you and apply that to your Google cloud or G suite.

Now what we can do is we can see how we can create your Google cloud instances from your console or from the come online, let's have a look in creating instances using GCB consoles now that we have learned on some basics of Google cloud Black.

One of the different services, what are different domains basically looking at Google cloud console or even cloud shell. Let's go ahead and create some b M. Instances now that's from your compute engine service and let's strike connecting to these instances and see how this works that's all.

You'll see what are the different options, which are available when you would want to create the virtual machine instance, this year. So when you click on your dropdown from the top left and choose compute engine. So it brings you to this speech. So I can show that again, so you can click on compute engine click on VM instances and.

That basically brings you to this speech.

It tells you compute engine that you use virtual machines that run on Google's infrastructure. So we can create micro V. M. So large instances running different distributions of Linux or windows using standard images that is public images and you can also have your own image. So let's create a M <unk>.

Since by clicking on create now that's basically helping me to create an instance here. So it shows me. The instance name I can give it something so let me see a C. One no I can add labels. So what is labeled it basically allows you to organize your project at arbitrarily labels SQL loop Astra Yoda.

So it's just so this is basically categorizing your labels and project. If you have multiple projects now remember if you have your cloud console and if you created your free trial account. It will allow you to do these things if not you may have to go back to the billing section and see the billing has enabled which also mean.

Instead, when youre, creating at Google Cloud account. It will ask you to keep your credit card details, but they do not judge anything or they might charge might be $1. One rupee, depending on your location and that's also the funded but that's just waiting for your card no. Once I've given the name I can choose a region.

So I would basically choose Europe's since I'm in Europe, I would be clicking on euro mystery and here I can choose an availability zone availability zone is basically to make your services or instances on any of the resorts.

Hi, Lee available. So you can choose one of the availability zones no I would choose <unk> III now. This one begins called down and he added sales machine configurations. You have general purpose machines, you have memory optimize machines, which is M. One cities and you can always go back to the Google Cloud page and see what April.

Kind of machines specializes in so I would click on general purpose and in general, but because you have different categories. So you have and one which is powered by Intel Skylake CBO platform. Why do you have <unk>, which is hubris platform selected selection based on availability. So let's have one selected now this one shows me that.

Machine type.

And if you are using your free DNA account then you can start with collecting a micro machine, which is one virtual GPU code you can go for the G. One small which is one much on CPU quota and 1.7 G. Vietnam Youre going to even go up to a high end machine and then you can basically see if you are using.

Free account how many of these machines you can use if I would we selected eight what youll see bugles and 32 gigabyte of memory and then it would allow me to create at least two instances might this configuration.

We'll use and one standard which is one what you'll GPU code seaborne seven five GB memory. Now then we can also deploy container image to the B. M. Instance, if you would be interested in deploying our container demand so let's not get into that right now.

It shows you the boot disk and it shows you D B N G and unite the next nine what I would do is I can go for this distribution all I can choose a Linux distribution of my choice. So here you go public images you have custom images. You also have snapshots. If you have created backup of your previous images.

Kim I can choose for example wouldn't too and then I can choose a particular, Washington, So let's go for the one to 18 point or four youre going to go for the latest one also it didn't winds it'll Florida would you put enough and hid. It tells me what is a boot disk. So you have it says to you. So you have standard persistent disc.

Ssds are literally expensive in comparison to your standard but system disorder Sdb's, but then it says these are fostered so as of now we can do standard persistent desk as it is and we can let the gigabyte B Dan now depending on your requirement you can increase this you can even add.

Disc slated that's not a problem click on select now she is I have axa scopes. So here I will say Hello default axis. You can also set access for each API. All you can give full access to all cloud Apis. So that based on the requirement you again anytime change later also.

We would also say L O S. T D be traffic and we can also choose L. O S. T D. B S traffic that basically allows me to access this machine our services would that actually be based accessible from this machine now I can just click on create Hollywood it would be good to basically enable connectivity into this machine.

Now we can do that in different ways. One is when you bring up your machine. It will have an SSH axis, which you'll get in Logan from the cloud platform kit itself on what you can do is you can create a private and public key using some software it's like <unk> or could be Jim. So for example, if you did not have that.

On your machine you can download you can just I'd been download body that takes you to the $40 odd page and here you can click on download and scroll down which shows you 40 or 64 bit machine, which I have in my case, you can download <unk> dot EXE, which is basically U S. S. Intel.

Clients to connect to your machines. You can also use would be gen, which will be allowing you to create a private and public key which I have done in my case, Let me show you. How so what you can do is you can go to be Jim to begin with and here you can click on agenda right. Now then to have the <unk>.

They did just move your card says on the top here and empty space and that creates Yogi you can give it to me. So for example, let's give a user name I would give HD you know I can give it password for this one so let me give a simple password.

And what I can also do is I can copy this public key from here and four of my later usage I can just keep it in my note that file which I can use later and I'll show you in so now we can see display with key and this will basically allow me to say.

But I would key I can choose desktop and I can give it a name so lets see new key new key and that will be getting saved an adult ppg's filings, so, let's see where it and that stuff. So we also have our public <unk> and we have saved that play with <unk> now we know that when you would want to connect using SSL.

H you need your private keys to the client and public. He also has to be existing so let's take this public key so let's do a control a I'm going to copy this and I'm going to come back to my Google Cloud console and here you can click on security. So you can click on the security tab here.

They're all down and just give you a public key once you give that it resolves and shows you. The name and this is good enough so that I get and use my answers its clients to connect to this machine I can click on create and this will basically create my b M. Instead.

Instance, it will take some time and then you had instance will have an intermodal a b, which will show up here external IP and it will also show you options to connect to these machines. So this is my internal I b.

This is my external might be taken used to connect from a client I can easily connect from the option kids. It says SSH and I can say open in a browser window I can even open this in a custom import I can look at the G. Cloud command, which you can give them cloud shell audio can use I know that I said.

So, let's first do it open in browser window, and let's see if disconnects. So we can easily connect go out instance, the wouldn't do 18 instance, which I just set up here easily in a couple of seconds. This.

This is trying to establish a connection using your SSH keys and when you do that it also basically brings up this web browser. So I'm already connected it shows my user name, which is my cloud account user name and I have been connected to this machine Kid using SSH and don't ask me for any password.

And basically now I can just check what do I have in my Linux file system, right and I can anytime loganair route by doing a pseudo issue for example, it straight installing it package and I can see advocate install them on advocate installed W get an aggregate <unk>.

On open SSH and all these packages are already existing so not an issue.

In stock using this instance.

Just look at the desk what is available. Okay. Now we gave 10 gigabyte out of which we see eight two gigabyte here for the day of his D. N. One and then 1.8 gigabyte available and you can continue using this machine. This was the easiest way of connecting to a B M instance, using SSH now what we.

You can also do is I can just leave this and I will now try to connect using an external SSH claimed and.

And here you can copy the public IP. So when you want to connect to an instance, you will have to get the public IP also remember that if you select and stopped this machine, which will stop your billing counters and if you started a game the internal I B will remain same but the external IP is the one.

Which will change.

I can't obviously select this machine anytime and I can do it cleaned up and I can believe it.

Again do a start of the machine if it has stopped and I can even in both the virtual machine to be used later, so there are different options, which you can always use. So this is my instance, and if you would want to look at the details click on this one C. One and that should basically and you just look at the lead.

So it shows you what does the instance, I D. What is the machine type is it reservation specific what does the CBO platform, what's the zone and all the other days.

At any point of time, if you would want to edit you can always click on edit and you can change the details as you neat now you can also look at the equal interest come on to basically use that is tpa to lending to this instance for now I have copied the public IP and I would want to connect it using <unk>. So let's go into here.

And let's give it the host name so I can give when do I can give my IP address so that's in my session now I will click on S. S. H I would go into authentication I click on browse and this is where I need to choose the BP gave five. So this is the one which we created new key let's selected.

<unk>.

And then I can come back to recession I can even save this and I can call. It as my instance, one.

Save it and you can create any number of instances. So you see I have created different instances here for my Google cloud and Amazon related incentives and I can get going to open. It says the sun was whole skis not cashed in that I just see that's fine just click on yes.

And basically it says no authentication method supported now this could be because we have not enabled you up and says the taxes. So let's look at that so now let's see if we were trying to connect to using <unk>. What was the issue here. So if I go back to my.

Select My instance, loaded it say sport 20 tool and giving the user name, which is wouldn't do Oh, sorry, that's the wrong user name of the game and that might be the reason, we had set up to use it as do you. So let's save this again and now let's try connecting to this one and it does sort my password.

And you are able to connect with US right now if there was any other issue related to network connectivity. Then we could look at the rules the inbound and outbound rules, which allow us to look into the machine now we are connected to a machine here using HD you use it I can login as root.

And I can kind of do work.

So not only from the SSH within the cloud console, but you can use an external it says its client and going to your machine. So this is yoda window machine and we can basically look at the space and that basically confirms we are connecting to the same machine, which shows eight two years of age hit in one gigabytes here, which we were seeing from the.

As such in the browser now, let's close this and let's go back to our instance speeds now Kid you can always look at the network be days and this will show you a different kind of rules that is in grants an eagerness rules, which basically allows to connect to this machine from an external network onward.

This machine to connect to an external network. So appeared we have different firewall rules, which shows default L. O S. T D b.

English rule and it tells you apply to all it shows me one of the IP ranges, but I can't specifically give the idea of money machine. It shows the protocols. It shows what are the different ports that you have used for these services. For example, RVP are they said surge, which shows 22, you have icmp STP.

B and S. G D. P. S. Now anytime if you would want to make a change to these rules that can be done by going into your network details.

Say for example, you would want to work on firewall rules. So we have these <unk>.

Firewall rules here you can click on this one so right now we are looking into the network domain and we are looking into BPC network right and this shows me one of the different roles. We have now if I would want to create a different firewall rules for a different protocol.

Always click on create firewall rule I can give you the name Okay I can see.

If you would want to turn on the firewall logs you can basically say what is the kind of traffic. So English applies to incoming traffic and eagle as a place to outgoing traffic and you can then basically choose what are the IP ranges from where do you want that connection to be coming in or going out you can choose.

A particular protocol you can give me a protocol kit with Docomo separated values and you can create a firewall rules. So this might be required depending on the services Mitch you odd Fleming Madden you might want to enable us access to your machine from an external service are doing.

External service. So you can always go to network details from Hugh you can then go into firewall rules create a firewall rules apply that to your instance, and restart you had instance, so as of now we don't need to create and it will be dense here because my English or eagerness rules are already available right now I guess.

And basically then have my instance stopped and removed. So I can just with stop our census is running the ideal way would be to do is stop I could also do a reset and what does that he said he said does not basically delete the machine what it does is it does a cleanup of the machine and it brings it to the initial.

State so sometimes we might have it installed certain things on a machine, we wouldn't want to clean them up and at that time of the Sip can be useful I can basically click on delete right and I can say like this and I can do a cleanup and this is good always when you're using a free trial account tried to use difference.

Services play around with them and then you can clean up so that you do not waste your free billing credit right and you can use it for meaningful stuff now I have clicked on delete and within a few seconds. My instance, which I had created will be deleted also to remember is if your trading multiple instances.

Then you can connect from one machine to machine using SSH by using the private fight so for that began loan it would be the latest. So this is just a simple example of using your compute engine, creating or instances connecting to it from an internal S. H R.

From an external necessity claims such as pretty bad you have already created your plywood and public geese now I can click on the browser to queue and then I can basically come out and I can basically be looking into any particular service. So we just looked into core compute engine right you have different other options you have.

Instance groups you have instance, templates for example, let's click on and since templates here and that basically shows you that you don't have any instance, template and this basically facilitate safe. For example, you are working is edman and you would want to create an instance, template so that using that you can describe a VM instance.

And then you can basically use this simply to create different instances you can vote for solar tenant nodes. You can look for machine images. You can also look at your tests you can create a snapshots and you can look at different options here.

Come back here and let's click on home and that should take you back to your homepage, which basically shows you if everybody Glen E. B I E would you have used recent leave it shows me the graph here. So I can go to the Epa's overview right and that basically shows me if there weren't any <unk> if I was using the <unk>.

<unk> P. A to basically create a b M instance, and that's why we were seeing some spike in the graph. So this is a quick demo of using your compute engine service provided by Google Cloud and you can create VM instances and use those VM instances far to your application installation.

Any other purpose now that we have seen how you use your cloud control to create an instance, and also cleaning it up let's also understand how you can do using your come online options and let's see what it takes off what are the different come on switch you can use.

To create your own instances now you can always win when Youre, creating an instance, you can use compute engine, which provisions resources to start. The instance, so instance, basically has different states, which began to see when we are creating the instance, so you basically start the <unk>.

Instance, incidence moves into staging that is prepared for your first boot.

Finally in boots up and then it moves into let me. So when you look at the instance states, which we will create and sends and see it will basically have different states such as provisioning Mad at US soldiers are being allocated for instance, but instances not yet I mean, then it goes into staging sources.

Have been acquired and instance is being prepared for your first Boot. Then instance is booting up and running and if you aren't stopping and instance, it goes into being stopped status and it would be moved to the dominated option. You can also do it in the batting off instance, and finally you can.

Dominate your instance, or clean it up by stopping and then deleting it now when you say stopping and resetting. An instance, you can stop the instance, as I showed earlier, if you no longer need it but if you'll need for future use you can just use the reset option, which will basically wiped the contents of instance.

Or any application state and then finally, you can stop it and haven't dominated now when you would want to do that using your cloud control or we have seen the options now lets also see from the come online tools. How we can do it so can I have the cloud shale, which I brought up from <unk>.

And I basically opened it in a new window. So come online tool enables you to easily manage your computer engender sources in a friendly format than using your compute engine a b R E G.

G cloud, which is part of cloud is the key is the main come on here and then you can always autocomplete the different options here. So when you would want to create or work on G. Cloud you can just type in G cloud here.

And then for example, I could just say help to see different options of G cloud, which will show me different options, which you can use here now we would be interested in compute instances. So he could also do a G cloud compute.

Compute instances and then I can basically say create and then I can do a health now that should show me different options, which work with G cloud compute instances create command.

As expecting any instance, neat so youre Gould cloud SDK, which began set up on a windows machine odd event on the analytics machine is set of tools that helps you to manage resources and applications hosted on GCB that Google cloud platform now.

You have options such as G cloud, which I'm showing you right now you have <unk> and then you have <unk>. So that also can be used so you can set up a Google cloud compute if using G cloud.

Now what we can do is if we are setting up our SDK on all Windows machine. Then we would have to do a G cloud in it which basically initialize. This your configurations for G cloud.

Now here, we are using our cloud shell, which was started from the console and we don't have to basically give you a G cloud and it come on because it solidly initialized no you can always look at yours deepwater zone. You can look at your region what is being used all those things are.

I mean from the meta data, which is being used so for example, I could be looking at the metadata for my particular project by just doing a G cloud and then I can see come view.

And I would be interested in project in full swing.

I can just do a project minus info and then I can do it describe and then I need My project Ivy. So I can say minus minus project.

And I can get my project IV from here. So you can click on this one and that's my project I D. So I can click on this one and here I can just do it right click and if it does not based then you can do we control.

And then I can try to look at my project didn't fall. So this basically forgive me the meta data, which is by default said and we can always look at what other regions are what is the zone, which has been set so if it has been said so I'm looking at my details. It is showing me my SSH keys and.

Then it can be using your default region and your default available Jones. It also shows my user name and other details. So this is basically to look at the metadata, which is available now or at any point of time I can basically say add meta data I can basically choose metadata option and I can.

See my default region should be Europe, which I was choosing ugliest. So I can just bring up this come on again and what I can do is I can see here and I have G cloud compute project info and I did it described earlier now what I can also do is I can just say add.

Metadata.

And then I guess I'm supposed to say what does the metadata I would be interested in adding I can then see Google compute.

Default.

Region.

And then I can basically give a region for example, Europe and then I can say west three.

And if you are not well experienced you can always do it from an Sone board you can be doing it from kids. So I'm, giving you will cloud compute Google compute default region. Then I can also give a Google compute deepwater zone.

And then I can pass it on value for the four zones. So I can see a euro based fee and then basically I can give you an availability zone. So if it is basically giving me some air where I'm trying to passing these values. So I can just give it this way and then it basically says that.

If you can look into particular help to see what does the command, which you would have to do now I can basically do a G cloud in it hits initial configuration and this basically says that it is initializing my default configuration. It says it initialize disk.

Figuration from cloud shell you wanted to create a new configuration. So if I had to updated my my Tech data then basically I could just with cloud in it and I could be Z initializing my default configuration on properties, which I have Boston so as of.

Now I will not activate or change the default region, let's go for a simple way in which you can create your compute engine. So for example, I'll just say one and it is a D. Initializing. It asks me what is the user name and let's say like that it stays where it is the project and let's say like that.

And do you want to configure a default compute region and zone right and I will just say, yes, and basically then it shows me different options, which we have here.

So there are too many options here and then here we were interested in you know with tea celestial was 21.

And then that basically allows me to choose my region and my zone. So it also gives you. Some specification. So it says your project default compute zone has been set to you know best III E. You can change it might I mean G. Cloud Config said and you can give me a compute zone right. So.

And always give these come on I can get health information here. So I can just say G cloud config set and I can be specifying the compute zone. So I can say compute slash zone and I can give his own I can say compute slatch region and I can give it region name if I would want to do it.

Are the easier ways like what I did right now so you can basically do E Command G cloud and it and then basically it allows you to change your configuration offset default things here you've got all the times you can say G cloud config unsaid.

Basically remove a compute zone on a computer Egypt now there are different ways. So if you were working on a Linux machine you could always use and export come on something like this so you could do export compute Sunny cloud SDK and then you can.

<unk> compute on the school zone, and then give you a zone named our computer underscored region and give you an region audio could have added in your best Schatzi fight right. Now that is when you have your cloud SDK set up on the Olympics machine auto and Windows machine and you would want to specifically set a zone.

And the region for all your compute related resources. So we don't need to do that the default settings had already given him right and what we can do is we can start by quickly looking into G. Cloud compute instances options like and say Gee cloud compute in.

10 cents and then I can just do a list at any point of time, if you would need help you've been just with G. Cloud compute and then say minus minus help or I could just do a G cloud compute and that shows me different options, which I have here from which I use instances I can always type instances.

And again had entered and it shows me different options for you would want to do so I can initially just type list. Richard show me what are the list of what are the instances available and as of now we don't have any instances I can always do a list and then I can specify minus minus four.

Matt and then try to get the information and adjacent format or E. M. M. R minus minus format fixed.

So you can always do a list you can do a filter. So there are different options with your list and you can you can try doing a help here and that basically shows you what are the options. So for example, if I do it helps and it shows me bid list what are the things you can do so you can give it a name.

You can give me a regulate expression you can see in particular zone you can use a minus filtered. So there are different commands, which at available and you can always find all those options here as I showed you earlier. So now what we would be interested in is basically going for your compute instances.

I can always do a S S H and I can create an incense I can add and remove metadata right. Four main census by giving you a particular zone by giving you a particular region and if you would want to do that now here. What we can do is we can basically creep.

And instance might just giving a name for that particular instance, and we can then go back to our console and see what has it done so I can see here create so that's an option, let's see what does that do you do so it says okay. You aren't giving you create option, but you would have to give it a name.

So, let's say, let's call. It E. One and that will be the name of my instance, and this one is an easy come on from your cloud shale, which basically creates an instance, you will see the zone, which has been set it is a standard machine type. It does not three M table has an internal I b and it has them externally.

Now I have created an instance, and he is I can just go back and then I can just with refresh on this page and that already shows me. The instance, which have created and then you can use the same method to connect to it using an SSH. So I have an instance created from.

My cloud shell.

And what I can do is I can basically look into incentives and see what are the different options within sense. So we did a create right you have an option to lead we do they create you have an option delete you can be and deleting. The instance from the come online or you can use other options.

So you can do a list you can do is stop you can start so I can basically do is stop and let's say, even let's go ahead and do it stopped come on some pretty easy here remembers all you can always use the help option and now I'm trying to stop the instance, and then basically I can go ahead and delay.

So this is a simple way and I created a instance from the come online or from the console, which I showed earlier and similarly, you can be working with other options. So now I've created stop the instance, led through a listing to see if my instance shows.

It shows up right and it saves the status as stoping Nathan So you have stopped it it's in the dominated status and now what I can do is I can go ahead and delete it.

So it says this will be last how do you assure you would want to go did you just say, yes and that should take you know still eating your instance.

So this is a quick demo on creating at instance, we have already seen how you can connect to these instances using SSH now that we have created instances, let's also see how to use Google cloud storage service, which can be used to load data are.

Upload data. So for this we will have to look into your Google cloud storage options and here you can look and cloud storage. So let's go back here on the top which shows me different services, which we have here and let's look into storage. So this one shows me you have storage.

Option.

Now you can click on browser and that basically shows me you are storage browser, which shows me on the top you have options for creating a bucket.

Now Google Cloud storage allows you to start any kind of data here they.

The easiest and the simplest way would be by using cloud console. Although you can again used cloud shell and then that you can use your G S utility demand.

And G. As you tell basically has different options, where you can be using say for example, I would want to work on buckets. So it can be using M. B and then I can use my come online option to create buckets.

<unk> put in data that I can plows, it and I get access to data from come on late so let's create a bucket here, let's click on this let's give it a name so let's say my data important that's the name now I can click on continual straightaway I can look into all of these options.

I'm interested in so you can basically be looking at your monthly cost estimate in the beginning now I can click on continue all you can say choose where to store your data and this one shows you different options. So you have region specific.

Speaker Change: So let's also give the bucket name with a lower case, that's what is acquired.

Speaker Change: So coming back to the location type you can choose multi region, which basically allows high availability.

Speaker Change: U S bucket audio a storage option will be accessible across regions you.

Speaker Change: You can also give dual region and high availability and low latency across two regions. I can also say region specific so far I'll use kids begin just give region specific which again keep our costs low but in business use cases, you would be going for multi region. Now here you have location and I would have.

Speaker Change: Gainesville for say Europe, and less tools University Frankfurt. It is always a good practice that when you create your own instances when you believe you have storage.

Speaker Change: You use different services try to have a geographical region chosen and then you would try to put things audio services within the particular region within that particular zone, unless you would want to make it accessible and available across regions.

Speaker Change: I can then choose a default storage class. So when you say default storage class there are different stories Clos and each one is for a different use case. So you have standard which is best for short term storage and frequently accessed data you have near line. So this is basically best for backups and data access less than once a month.

Speaker Change: You can also have core lightened. So these are basically like Youre cold stone age on freezing storage, which you might've heard generally and storms. So you can choose one of these storage classes, depending on what will be the use case for this particular storage bucket. So let it be standard now how to control access to.

Speaker Change: Objects. So you can basically say specify access to individual objects by using object level permissions now you can give permissions to the bucket you can just see uniform access to all objects in the bucket by using only bucket level permissions. So you can choose that you can also go into Ed one settings.

Speaker Change: And here you can see different ways in which you can have configuration set up now you can also have a retention policy to specify the minimum duration read that there's buckets object must be protected from deletion and you can set the retention policy will not wait until all that we will just first try creating a buck.

Speaker Change: So just click on create and that should create your bucket Brandon I have my bucket now I can click on overview to see the details what is the region, which region. It belongs what is the storage class anytime you can always click on edit bucket and make some changes here you can.

Speaker Change: At the permissions, so but good news is fine grained access, allowing you to specify access to individual objects and then you can basically look at who has access to this in my case.

Speaker Change: And it is of the project. They basically are the buckets owners owner yourself project viewers of the project right and you can basically choose what kind of faxes you'd meet so far. This you can always go into cloud storage and then you can decide what kind of access you would want to give them whether that's in storage.

Speaker Change: Edmund it's an object admin object creators object viewer and so on you can always look at storage legacy <unk>.

Speaker Change: And for any of those services, depending on what Api's you have enabled right you can always control you edible machines. So right now it is storage legacy bucket readers and that's fine and this one is bucket owners and then might be I was using some other services like data rockwood use as cloud storage and that's why.

Speaker Change: I've given Ddos truck service agent also so these are some of the members you can remove you can view by specific members you can view by rules. So what are the different rules, which have access to this so at same store is legacy bucket on up there are two owners based on this particular project. So these autocar.

Speaker Change: But then you can add or remove machines, you can save storage costs by adding a lifecycle rule to delete objects. After the duration of current retention policy. So you can add different policies and you can basically control. Your bucket now my bucket has already created right. So I can go back and.

Speaker Change: Then I see my bucket is already here I can click on this option and you get any time ended the bucket promotions you can enter the labels default storage class you can just go ahead and delete the bucket you can export it to cloud bump sub so basically if you would want to have the content from this bucket being accessible.

And the message queuing system you can go for it bumps up you can process with cloud functions and he can scan with cloud data loss prevention. So there are different options, which are available began always select this bucket and delete. It now I can click on the bucket and that basically shows me different ways in which I can upload some data here so I get it.

Speaker Change: Just click on upload files and hid than I can choose some files. So for example, algo.

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