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transcript","richText":"\u003Cp\u003E\u003Cb\u003ERachel Roundy:\u003C/b\u003E Hello, and welcome to the Snowflake AI Pulse. This is our new series where we bring together the most important AI innovations across our platform, and more importantly, what they unlock for all of you. Each of these episodes is designed to really cut through the noise. We're connecting new capabilities to real workflows, whether you are building with data or you're using that data to drive decisions across your organization. Of course, I have to give a shout out to our legal team for our safe harbor and disclaimers. Anyone who's worked with legal, much love to them. I am your host, Rachel Roundy. I am the product marketing lead for AI here at Snowflake. And I'm joined today by some incredible guests from our product team. We have Pranav Pokharel, Will Allen, Avinash Joshi Joshi, Alan Yu, and Jessie Felix Felix. All right. And today, we're going to be looking at what's new. We'll get a quick glimpse at that. And then we'll go into the real reason you're here, the deep dives on these really incredible topics. Just a shout out to the audience as a heads up, we are going to be doing Q and A a little bit differently. If you joined us last month, you'll be familiar with this format. We're going to do the Q and A after each section. That way we can really be focused on which questions are meant for which speakers. If we don't have time to get to all of your questions, we will be answering them in the tool, and we can also follow-up offline. So please use the Q and A tool in the webinar platform to submit your questions. Alright. So before we jump into our deep dives here, let's think about where AI is right now. Until really recently, enterprise AI was really defined by experimentation. A lot of organizations were kind of prototyping, right, deciding if AI was going to be viable for their business. They were testing models, exploring use cases, but that has really shifted. The conversation today is less around proving that AI works and is valuable. The challenge is really around scaling AI across your enterprise. And the questions that we hear from most of you these days sound like, how do we empower developers to build with AI wherever they work? How do we operationalize AI at enterprise scale? And not just for demos, but for real business workflows with governance, shared context, operational controls, all of that built in. And as adoption grows, very hot topic, how do we optimize cost and performance? Solving these challenges takes more than an individual AI capability or model. It requires a unified platform. And when we're looking at organizations seeing the greatest impact from AI, there are kind of four foundational capabilities, that show up. The first is enterprise data and context. So as we all know, AI is only as good as the data and the business context behind it. And that means trusted, governed data that every model and every application can understand. The second is, of course, the models. We have to have the the brain, right, the intelligence there. The model landscape is evolving so rapidly, and enterprises and all of you need flexibility to choose which model is going to be best for each workload and use case, and not be locked into a single provider. And the third is software and applications. AI creates value when it connects to systems where work is already happening, allowing your users to take action on the intelligence using the context, where it already lives. And tying it all together is the agentic control plane, which is a single place to build, deploy, operate, and govern AI at enterprise scale. And together, these four components create the foundation of the agentic enterprise. And really critically here, they all operate within a single governance boundary. So whether you're building with CoCo, deploying managed agents, or running production ML workloads, every workload gets the same security auditability and cost controls. We've been really clearly listening to all of you, and we hear over and over, you don't want a dozen point solutions that are bolted together. You need one coherent governed AI platform, and that's really what we're building here at Snowflake. Today's innovations are going to really show how we're bringing that to life. Every feature that we discussed today was purpose built to help our customers, all of you, achieve your enterprise AI goals. So first, we're helping teams build anywhere. We'll show how CoCo and Cloud Agents make it easier for developers to build wherever they already work while staying connected to enterprise data and governance. Second, we're helping you operationalize AI at scale. From governed AI agents to real time ML, you can bring intelligent applications into production in a single governed platform. And finally, we're helping customers optimize cost and performance. AI function studio and per user per user quotas are going to help you choose the right models, maximize quality, and control AI spend as your adoption grows. All of these innovations strengthen the foundation of the agentic enterprise, and they're going to help all of you move from AI experimentation into enterprise wide adoption and ROI. So with that context, let's take a look at everything that we're launching today. So here are our coolest features hot off the presses. Now this is a quick glance at everything that we will look at in our deep dive section today. And I like this view because it shows you what's available, what can you sandbox, and what can you go use right now. The next slide is also important. You'll see in the webinar platform, there's a lot of related assets. Every AI pulse, we have more innovation than we can possibly fit into one session. You may have noticed that we had to expand the timing of this to ninety minutes, and we appreciate you all joining us. But there's too much goodness to fit here. So I wanted to highlight a few additional launches that will not be in the deep dives today, but they're absolute game changers. For dynamic type tables, we're introducing custom incrementalization, which lets you handle more complex transformation logic without giving up the automation that makes your dynamic table so easy to manage. We also have adaptive refresh, which automatically switches dynamic tables between incremental and full refresh strategies to minimize compute cost and prevent failures on complex queries and really eliminate manual tuning. Cortex agent sharing, we are making it easier than ever to scale agents across Snowflake accounts to internal teams, partners, or the broader Snowflake ecosystem via marketplace. And you can even add them to CoWork or CoCo with one click, which is pretty insane. With auto agents for shared data, you can instantly generate a Cortex agent or semantic view from any data listing or secure data share. And finally, with our Snowflake and Meta Ads integration, you can send governed conversion signals from Snowflake to Meta Ads with the Meta Conversions API skill. So if any of these are relevant to you, please check them out. There are linked resources in the webinar platform. Again, these won't be covered today, but they are highly important. All right. And with that, without further ado, we're going to get right into it. Our first topic today is cost quotas, and I will pass it off to our expert Pranav to take us take us home.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EPranav Pokharel:\u003C/b\u003E Great. Thank you, Rachel. Alright. Hey, everybody. Nice to meet you all. I'm Pranav Pokrul. I'm a PM here at Snowflake. I lead our cost governance capabilities that we offer to you all to make yours Snowflake spend healthy. So before we dive into quotas, which I'm very excited to talk to you about, just wanna give you a little bit of framing in terms of how we think of cost governance and cost intelligence here at Snowflake and provide the out of the box capabilities as part of the platform so that you can take advantage of this to manage your Snowflake cost. So we think of a cost governance in three pillars here at Snowflake. The first one being visibility. So the important thing there is first, we wanna make sure we give you visibility into how you're spending with Snowflake. This may be, using, various types of views that we produce, for you both in the account usage and organization uses schemas for for your reporting. Then comes the, in addition to that, we also give you dashboard capabilities through the cost management dashboard in Snowsight. And with Snowflake CoCo, now you can also ask CoCo questions and there are relevant skills in CoCo that will help you answer questions around your your costs. The second pillar is the control pillar. Now that you have visibility into your cost, how do we give you guardrails and notifications and alerts and governance controls that will allow you to manage the cost that is happening within the Snowflake platform. So that would include things like budgets and resource monitors and anomaly detection capabilities and what I will be talking to you about per user quotas. And then lastly, we also give you the the ability to optimize your Snowflake platform further by giving you recommendations. We have well architected framework that gives you best practices that really allows you to get the best out of Snowflake. Okay. So let's dive into per user quotas. So what are per user quotas? Per user quotas are is a feature that allows you to track spend for your users, at a per user level. So you can define spending limit for your users AI spend in Snowflake, and you can track your users, usage over time against that spend. And, depending on, the business workflow and outcome you're trying to achieve, you can put certain guardrails including being able to restrict users access until that quota cycle resets. So think of quotas as a tool for you to, one, give visibility into how your users are spending on a key AI capabilities as well as giving you guardrails where if there are one off users who are accruing undesirable spend from your perspective, you can put some guardrails to restrict them from, you know, going and and spending all of the kinda budget that you've defined for your teams for for your AI spend. So moving on to some of the details here for user quotas. Per user quotas today are in PUPR. We are working very hard to get this to GA to you very soon. So quotas will allow you to define monthly as well as daily limits for your users spend. You can also define both on the same quota. So you can set a monthly limit as well as a daily limit on the same quota. And when those quotas are met, you have the ability to either just be notified or you also have the ability to set guardrails to enforce restrictions from the user using beyond their their quota limit so that you're controlling their spend. And when those block enforcements are enabled, what happens is that the user is restricted from usage until the end of that limit cycle. So for example, if you set both a daily and a monthly limit and the user hits a daily limit, they are restricted from using the rest of using the feature until the rest of the day in UTC. Same thing with the month. If you hit a limit for the month, your user is restricted until the end of the month. And when they go to their applications, they're gonna see relevant information around the fact that they're restricted because they have hit their quota. You also have the ability to send them notifications. And not only can you send notifications when those limits are actually met, quotas also allow you to send notifications on prior thresholds of usage based on a customer's using or sorry, users using, whatever threshold of usage that you want them to, start being notified at. Key things to call out here, the AI features that we support are all of the big AI features that Snowflake provides, including AI functions, CoCo, CoWork, Cortex agent, and, quotas can be defined through Snowflake UI, and I'll I'll I'll show you a quick demo on that. It it can also be defined by SQL. And even better, if if you're on the, the CoCo flow, you can also kinda define and manage quotas and ask for analysis of quotas, using Snowflake CoCo itself. One another key thing to highlight here is, you have a couple of different ways to set up quotas. So if you wanna target all users and set the same limit for all users, you can just, select all users as your target for your quota, but many of you wanna have different quotas for different teams. And with even within the same team, you may have a standard quota and a high tier quota for some power users. In those case, those users can be separated out in different quotas and and targeted based on Snowflake user tags. So if your users are tagged with the relevant tags, you can use those tags as the scope of who you're trying to target with the quota. Alright. How about we quick like, look at a quick demo here. So let me go ahead and share my screen. K. Hopefully, you all can see my screen here now. Okay. Great. So this is the cost management module within Snowsight. The way to get to this is you can go to your admin cost management module, and it will bring you to this module. There are various tabs in here including being able to view your, account spend or or the overall organization spend as well as some other features like cost anomalies and resource monitors. We're gonna focus on per user quotas. So get to get to per user quotas, you land on this budget step where you can see an aggregated view of your budgets that can be defined in it, for, like, a, like, a team level spend tracking as well as create quotas that are tracking per user spend. So I'll go ahead and create click on quota. It will bring me to this workflow that I can go through. So, in the quota scope, I first get to choose who am I targeting. I can target all users within a given account, as I mentioned previously, and and exclude certain users within that, target scope. Or in this case, I'll go ahead and select a group of users. So I've already tagged my users in various departments. So I'm gonna go ahead and say that I'm trying to create a quota for my engineering group here. So let's select that target group, and, now it's gonna go in and fetch all of the relevant users that are targeted by this user tag. Once I've selected my scope of which users I'm targeting, I get then get to define what features, I'm trying to set this quota for. We also support warehouse based quotas, but in for this demo, I'm focused on my AI, features. And within the AI features, I also have the ability to either overall group spend across multiple capabilities here or, do it for individual, capabilities. And for things like AI functions, you also have the ability to target specific AI functions like complete or overall AI functions. So for this demo, I'm gonna say that I'm trying to target the AI functions and CoCo spend. So I'll select just those two. Then I I have to define the quota that I'm trying to create. What's the name of the quota? So I'm gonna say this is my engineering CoCo and AI functions quota. I have to choose, so the quotas are objects in Snowflake. So I have to choose a location to store those quotas in. So I'm gonna go ahead and find a database and a schema where those quotas should be stored. And I now can define a monthly and daily limits for this quota. So let me go ahead and say that my monthly limit for each user is two hundred credits oh, sorry, two thousand credits, and my daily limit is a hundred credits. And as it states here, it resets, at zero zero UTC every day, and then the monthly limit resets every at the first of each month. I'll go ahead and click low latency quota so that, like, my updates are faster. I now get to set some alerts. So I as I mentioned previously, you don't have to wait until the quotas are actually met. You can also add alerts based on actuals. So here, if I've defined this so that if I hit eighty percent of my actual spend, I can notify my end user as well as get notified myself. And then I can also add, alerts based on projected spend. So even though my users haven't hit a hundred percent of their quota, I can set up so that, like, if if the system projects that they are gonna hit a hundred percent of the quota, I want them to also be notified. And I wanna set, emails so that I'm also notified. And if you have multiple admins, you can have a comma separated, set of admins who are notified. Another important thing is this option here that allows you to enable So by default, Snowflake will not go ahead and restrict access. You will have to state that when those when the quota is actually met for your user, you do want them to, be restricted from accessing features. In this example, that was for AI functions in CoCo. And if you do that, then when the quota's hit, it is gonna go ahead and, track the users gonna spend to see if it's going over quota every, few minutes, and then and then it's gonna, put in the the enforcement for that user for the rest of the quota cycle. So go ahead and click on create here. So, typically, it takes about ten to fifteen seconds here, for, all of these configurations to now be stored so that your quota is eventually created. So let's wait for that. K. There we go. So the quota is created. It will take a few minutes for all of the usage against this quota to show up. So in the interest of time, let me show you a quota that I already previously created that already has some spend that it is tracking. So as you can see here, once you start tracking the user spend, it's gonna actually give you this, like, really good visual in terms of, like, what's the spend usage look like for your users and who these users are. It will also tell you, like, how much quota is remaining for these users overall. In this example, some users have gone way over, but most of the users have not used at all. I I targeted all of the users in this account, so it's targeting forty five hundred users. And that's why for most of them, there is no usage. So so average utilization is still pretty low. And in this example, I haven't enforced enabled any enforcement. But if you were to enable enforcement, then you would be able to track which users are already blocked versus which users are also are not unblocked yet. You can see some additional details around the quota configuration that you just made earlier. Here, like, there's a user that, we've added as an exception to our quota, and then this quota is targeting all of those capabilities that was showing you earlier. And if you go and scroll down, you can go to, this detailed view where you can see, details of, like, what's the users, kinda spend against their monthly utilization or monthly target, what's their spend against their daily target, and then, like, what are, like, relevant tags they're matched with. So you can you can do a lot of analysis, like, through this this page and get into more details for your specific users. Lastly, I want also wanna call out that you can do the same things via SQL. So this is just in script that I have as an example where you can set your resources, you can set your limits, you can enable enforcement. All of that, you can also be done via CoCo sorry, SQL and, as I mentioned, also. Okay. Great. So now with that, let me hand over to Will to talk about managed agents. Alright.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003ERachel Roundy:\u003C/b\u003E Actually, nope. Sorry, guys. Oh, sorry. This is real time. Q and a. We're yes. So we're real time changing the flow, keeping you on your toes. But I appreciate you jumping on, Will. But, yes, Pranav, we have a couple of questions for you first before we get into the awesomeness that Will's gonna share with us. So the first question, and I think you touched on this, but just to reiterate for the audience, related to cost control, is there a way to put a hard backstop on the number of credits at a user level per day? He's looking for something to instantly stop a runaway query, say if somebody accidentally runs an expensive AI query on all rows of a very large table.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EPranav Pokharel:\u003C/b\u003E Yeah. So as I showed you earlier, the enable enforcement option do allow you to put those kind of hard restrictions so you block access. And so the thing I would call out is that the the quota limit you put is not a, like, a top limit. Right? So we are the quotas are executing based on actual spend that occur. So by the time the spend calculation happens, there the user may go over by, a little bit of of the they're gonna spend limit, but the enforcement tracking is happening every few minutes. We're we're generally in that two to three minute time range. So it's gonna immediately kinda track, like, hey. Has my user gone over? And then if it finds that the user has gone over, it's gonna put in those enforcement for the rest of the quota cycle.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003ERachel Roundy:\u003C/b\u003E Awesome. Thank you. And do per user quotas work for service accounts as well?\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EPranav Pokharel:\u003C/b\u003E Yeah. So any user that has a user ID in Snowflake, the quota will track spend against that user ID.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003ERachel Roundy:\u003C/b\u003E Awesome. And I think that's related to this other question. Will the Snowflake provider also be updated for these new new cost control capabilities? So it sounds like everyone who has a Snowflake login has this capability. Fair to say. Will the Snowflake provider also be updated? I'm trying to understand what that means. I think, like, service providers that do certain Snowflake service services for other companies.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EPranav Pokharel:\u003C/b\u003E Yeah. So you can so if you're the admin for that account, you can configure the quotas as an admin, and you can be notified. I showed you earlier I put in my email address. You can have multiple admins be notified in addition to just the users themselves getting the emails.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003ERachel Roundy:\u003C/b\u003E Awesome. Okay. And one last question. How do per per user quotas work alongside AI budgets and how would you think about using them together?\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EPranav Pokharel:\u003C/b\u003E Yeah. So quotas are really trying to track spend at a per user level. Right? So that, quotas are relevant to you if you wanna prevent kinda runaway cost for specific users. And quotas allow you to do block enforcement, you can effectively, just go and block that user. Budgets are for your tracking purposes so that you can track, like, team level spend. So let's say you're, trying to just, like, understand, like, how much is my engineering department spending versus finance department versus analytics department. In those scenarios, I can I can kinda use budgets rather than kinda like putting in limits for specific users? So that's how I would think about it. With budgets, we don't given it is targeting a large group of users, we don't have out of the box enforcements there. You could build out your so it does allow you to trigger custom actions. So you could build out your enforcement, but the disruption there is much larger because, again, you're track kinda tracking spend for a group. But, like, if you're trying to really limit, like, one off users from going way over what you're comfortable with spending, what is the way to go?\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003ERachel Roundy:\u003C/b\u003E Amazing. Thank you so much, Pranav. This has been an amazing section and such a great topic. Thank you. And now we are going to pass it over for real to Will Allen to talk to us about managed agents. Over to you, Will.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EWill Allen:\u003C/b\u003E Amazing. Thank you. Honestly, one of the most important things that we've been working on are those cost controls. I think they're really, exciting. Everyone everywhere is thinking about sort of ROI on their AI spend. So these are, phenomenal updates. Excited for these to be out in the world and to get your feedback on those. But alright. Let's talk about agents. I imagine all of you out there have built and deployed, a couple of agents, hopefully, many of those on Snowflake. But what is happening now is really understanding how to operate these things at scale. Not just a singular agent or just a couple agents, many, many agents, lots of integrations, running them at scale, all with the sort of the right orchestration, execution, and governance across the board. That is a it's a big challenge and something that we're thinking about every day in terms of how do we enable you to do that, all accessing your data as it exists inside of Snowflake, again, with the right governance execution, orchestration, and importantly, cost controls, across the board. It's the new world that we're we are living in. We are announcing a number of sort of new features that are rolling their way out, many of them public preview, soon, a couple of them GA. I'm gonna talk about a couple of these here, all avail available via the Cortex Agents API. We are pumped about many of these. On the orchestration side, first and foremost, probably the most exciting thing, sort of a real coding tool set. So using the same Cortex agents API, you can basically have managed CoCo runtime for any app or any application that you were building out there. We've got a blog that's coming out today that will have a lot more details and sort of how we're thinking about this, how to make it work. But really imagine bringing sort of the power of of Cortex Code of of CoCo into your application via the same API that you're using to build Cortex agents today. We think this is a huge update. We're pumped about it. Tool search. Managing a context window, as we all know, is one of the most important things you can do when you are leveraging AI or your agent, is out there in the world. We have a new way to sort of do tool search and really thinking about sort of discovering tools on demand instead of loading them all up at the at the outset. We think this gives is gonna give you better performance, and have a much better sort of context window management, for any agent built there. A skills package, the way to sort of have curated skills as a single object that you can leverage across the board, that's also coming to public preview soon. On the execution side, an async agent API. So imagine being able to run long jobs, excuse me, with no connections held. This is going to GA soon. Really important update. A very requested feature that we're excited to get out there. And how many of you, when you were either building an agent or using an agent yourself, need to stop it and need to come back later, and to resume it as you are the human in the loop? I do it all the time. That's now gonna be GA inside of the Cortex agents API, very soon, a much needed feature that we're pumped about. Code execution. The thing is, and we all know this very well, is that agents are incredible at writing code. But you don't always know what they wanna have, and you wanna be able to make sure you can execute those in a fully sandboxed environment so you can take advantage of their ability to write code and using things like Python and polished outputs, but not worry about your actual production level environment. We've got our own sandbox via code execution. This is rolling out to public preview soon. We've been dogfooding internally for many months, and many customers are using it in private preview today. Fantastic, new feature that really sort of opens up the powers of what a a Cortex agent can do. Versioning UI, really helpful as you're thinking about sort of making updates to your agents and rolling them out to the world. You can build one, test it, run your evals on it, create a version, ship it out there. If it doesn't hit your evals or something change, you can always roll it back. It's kind of a table stakes for great agents, we think. Another one is partial access. We think this is a fantastic feature. We've heard a lot from customers that they have they've spent a lot of time really cultivating and curating a singular agent, that searches across their, all of their Snowflake data and understands what's going on across their business, but they wanna have different levels of access for that particular agent. Maybe one, person has this particular role and another person has a different role. They don't wanna have to recreate the agent for each role. Instead, they wanna have partial access across the board. So saying, hey. If a public role interacts with this agent, give them access to x y z, semantic views in these parts of the agent. But if an engineer role or a different role interacts with it, give them sort of broader access. Again, makes your job easier as the agent builder and keeping you inside that security parameter, really important, and a great thing there. And a very similar concept with, actually, with agent toolset, the ability to sort of, like, define once in an agent. Here are the set of tools that my agent use, but then reference that across the board for new agents that you're building. We're really trying to think about the things that you do on your day to day life when you're building, deploying, and running agents at scale and how how to make that easier for you. So a lot of these are sort of rolling out to either public preview or to GA soon. We are pumped about all of these and cannot wait to get them, in your hands. A little bit more on sort of bringing CoCo into the manage agents API. It really allows you to sort of think about, again, taking CoCo and building it into your own applications. It knows deeply about your, Snowflake environment. It can do all the great things you need to, SQL execution, data transformation, pipeline automation, and then you get to expose this if you want via the Cortex agent's REST API and then compose it as part of your bigger workflows. We are using this heavily internally, as we do with all of our products and features, and we're particular, very pumped about this when I get it out into the world. The other one, just to go, like, a a touch deeper, is sort of this notion of, like, both the async agent API and the interrupt and resume. Not having to keep a single connection open, for the async agent API, really helpful. When instead of having sort of a singular turn that you can pick up later, it's just a part of so many workflows and allows you to do things, you know, like generating a research report, a batch of contracts, and it allows you to sort of think about real world scenarios for your agents that doesn't where you you require you to keep that single sort of thread going consistently. Same thing for interrupt and resume. Again, in my personal workflows, I'm constantly stopping things, tweaking, and sort of reiterating those. Now you can do the same thing with any Cortex agent that you build and deploy out into the world. The ability to sort of, like, stop, say, hey. Let's pause a second, have the human fully in the loop, and then to resume in the exact same spot. Another really foundational part of the of the agents API. These will be coming out, both GA relatively soon. Code execution tool, talked about this a little bit. Really powerful. Again, agents are amazing at writing code, but you gotta put them in a sandbox to make sure they have the right sort of, guardrails all around them. And so we've spent a lot of work to make sure that we can do this to really unlock the power of writing sort of ad hoc code and keeping all of your, data fully secure. Starting this with, you know, with Python and Nummies and Pandas, and we'll be extending this pretty soon to more libraries and more references. But, really, we think this is a massive unlock across the board to take agents to make it a fully managed agent platform that allows you to build incredible applications, starting with your Snowflake data, moving into the coding applications, by using sort of, you know, a managed CoCo runtime, and then doing things like the code execution tool to really take advantage of the power of these frontier models. So that's a lot of updates about what's happening on sort of the managed agents API, the Cortex agents API. Again, we are listening to you all about the the key things that you're you're building, you're thinking about, and excited about, both these features and many more, that are yet to come. So I'll I'll pause here And see, Rachel, if you've got any questions or things that we can answer.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003ERachel Roundy:\u003C/b\u003E We sure do, Will. We sure So this first one, is it possible for agents to access other agents outside of Snowflake?\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EWill Allen:\u003C/b\u003E Yes. Yeah. It's a great question, and you can do that today. So you can, as you're building an agent, a Cortex agent, and if you're actually if you're in the UI, if you're inside of Snowsight or if you're using CoCo, you can connect to them via MCP. And so we have many people who are stringing together pretty interesting workflows where it's effectively a tool call out to whatever that other agent might be. Maybe it's a voice agent. Maybe it's a I don't know. It's your accounting agent or something else that's out there. We think it's a it's a critical part of that workflow. You can actually build almost effectively like a shim on top of that. If you wanted to do an an a to a protocol, that works as well. We've got an open source repo that you can grab and, and look at. Very common workflow that we hear about quite frequently.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003ERachel Roundy:\u003C/b\u003E That's awesome. Okay. Next one for you, Will. How does governance work when a managed agent invokes multiple tools or accesses different datasets with different permissions?\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EWill Allen:\u003C/b\u003E Yeah. You can it's all inside of the agent object. You have lots of granular control over that particular agent who has access to it and who has access, you know, the right privileges for the underlying either semantic views or tables or or schemas, the other parts of sort of the, or the Cortex search service, like, the core part of it. We have a nice little verify access button inside of the UI if you're not doing this inside of CoCo, though I always recommend using CoCo. Your life is, better and easier when you do that. But inside the UI, you just, like, click verify access. You can actually test particular roles. Hey. If this is the public role or if this is an engineer role or an account admin role, do they have the right privileges to access all of the underlying data? And then we are working on ways to actually make that, even more sort of easier for you across the board. But again, bound inside of the agent, the agent defines the parameters, and then you make sure the privileges are associated with the roles to be able to interact with that agent.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003ERachel Roundy:\u003C/b\u003E Awesome. Okay. Now this one's kind of a meaty one, but in a in a in a short answer. Yeah. Can you talk about multi agent orchestration? What's possible today and what's coming?\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EWill Allen:\u003C/b\u003E Yeah. We are working on this extensively across the board. So on the the the one side, again, you can do this via MCP and eight to eight today. So you can have a singular Cortex agent, and you can call other agents from inside of there, via MCP. That works today. We have many customers who then invoke a Cortex agent via sort of a a higher level architecture, whatever their orchestration layer, either one that they're working with us to build or they they've rolled their own, perfectly valid, and they so they have their own orchestration layer, and then they can invoke particular Cortex agents points in time. We're doing more work to actually make this basically seamless for you. So you can really think of sort of, like, both model routing and agent routing, the Cortex agents API level that allows you to sort of not only have to do it via MCP, but sort of smarter, like, drag and drop sort of orchestration levels. We can say, hey. Here's my my primary agent that I wanna have out into the world, and then this agent needs to invoke these. And I don't only wanna do it via MCP. Maybe I wanna have a little bit more deterministic workflows that are out there. Again, we're dogfooding this internally. We've got a couple of customers in private preview that we're working with us on. Excited to get this out into the world. So the TLDR, MCP today, invoking these via your own homegrown orchestration framework, and we've got hours that we are working on, which we hope to talk about more very soon.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003ERachel Roundy:\u003C/b\u003E Yes. Watch this space. Keeps keep coming to the pulses. We'll have more to share, I'm sure. Well, thank you so much, Will. Thank you for covering all exciting updates with Managed Agents. We appreciate you. Thank And we're going to pass over to Avinash Joshi to talk to us about online ML. Avinash Joshi, welcome.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EAvinash Joshi:\u003C/b\u003E Thank you, Rachel, and hello, everybody. I'm super excited to be here and chat about our online ML capabilities and, in general, the ML platform. That, let me go to the next slide. So what is ML platform in Snowflake before we jump into online ML? Right? So this is a great slide for looking at it as a complete text our complete tech stack when it comes to machine learning. So the machine learning in Snowflake is now an end to end platform in which you can serve your entire model's lifecycle right from your training and experimentation to managing your objects and data to deploying them for production and then monitoring them. So if you see look at this if you look at the slide here, it up top are our interfaces that we can use for interacting with these ML features. Up right up top is our Snowflake logo. I'll talk more about that in in a second in the ML context. Next are our Snowflake notebooks and our external IDs. So you can choose either to use our Snowflake notebooks tool to access these features, or you can create a session object in any of the external IDs that you prefer and use use these features you using the using your ID your ID of your choice. Using these interaction interfaces, you get to access all the feature sets in the third in the third row, which is firstly from the for the development and iteration of your models. You have your Snowflake ML APIs for training your models in the most optimal fashion, experiment tracking where you can track every every model run that you're doing along with its relevant metrics, parameters, and artifacts, and the source files. And finally, cortex training, where if you need to fine tune your these big LLM models, you open source LLM models the most efficient manner. Next is orchestration and automation of these training pipelines. A lot of times, you don't want this after when you get it to a point where your expectation is done, now you want to operationalize a training pipeline. You can you have we have something called ML jobs that let you define these over define these training pipelines that are done on massively distributed frameworks. Once you have your models and features shaping up, we have a model registry for managing your model's life cycle and a feature store that helps you define your feature transformations and then share and collaborate with your teams. After that, you deploy these in production through model serving, both in batch mode or in online mode. And today, we'll be talking more of the online portion of it for both the models and the data. And finally, once you have a model in production, anything in production needs to be managed and measured. So we have our ML observability, which lets you monitor the model's performance and drift, and explainability that allows you to hey. When the models go as go awry, what what has gone wrong? Explain its behavior. All of this runs on our two compute compute platforms. One is the ML optimized warehouses that that are great when the or when you want to integrate with your existing SQL pipelines like DBT, etcetera. Or you have your we have our container runtime on this on Snowpack container services, which brings you the latest and greatest frameworks and DPU access, etcetera. So based on your use case, you can pick your compute workload. So the first interaction point I was talking about, which is CoCo, and you've already heard a bunch about agents and how how these things are evolving at Snowflake. But as these agents get better, as CoCo gets better, it's also great at doing your ML tasks. It's an end to end ML automation tool, which automates your entire ML pipeline, right, from data prep to feature engineering to evaluation to deployment. It has the iterative reasoning functionality, so it it not just gives you a scaffolding model of a code. It actually runs the code in a sandbox, make sure it works, make sure it works with whatever policies that have been set up for your Snowflake account, and then gives you the notebook. And the best part about CoCo is when compared to other Copilots or the cursors of the world is it lives and breathes within your data platform. So when you ask it a question, hey. Build me a fraud detection model. It's not going to start with, hey, which database, which table, which schema, explain these features to me. You don't have to do any of that. It's already already there. And any semantic views that you have set up are already accessible to GoPro that can just start it can just get going and give you put you on your part to your end notebook very quickly. Now to talk about specifically about the features itself is across all three categories, training of models, feature engineering, and feature store, and finally, the model predictions themselves influence. On all three categories, we are much better than our competition. So, gone are the like, we are not only at parity when it comes to platform, but we are beating our competition hands down when it comes to both performance and cost effectiveness. So I'm not gonna spend too much time on this, but just to give you an idea, the training actually, we can train our PyTorch training is two point five x faster than three x less expensive. Our feature store is two point five x lower latency than our competitions. And after after a point, some of our competition doesn't even work because of the throttling. And we have seven x higher QPS. And finally, when it comes to inference, both for traditional models and your open source LLM models, our latencies are much better than our competition. Now to come to the meat of our topic today, which is real time feature serving. So if you so today, as I was saying, we already have a feature store, and we have a model deployment in inference. So what am I talking about here? Before our current feature launch, you if you have a feature store and you have a real time inference service, the real time application first would have to make a a rest call to the feature store, fetch the latest data that has come in for this entity, whichever entity you are looking for. Then, again, make another REST call to the to the inference service so that the inference service now gets the features that it needs to make its prediction. This would mean there are multiple issues with this problem. First and foremost is what we call the logic duplicity. So you are training a model using a feature store. But when you go to production, you're not passing the data from the feature store but trying to directly give it data. High chance of something going wrong there, where you some feature that was defined a certain way doesn't translate well to production. Second is you're paying the late you're paying a double latency penalty because each request call has to go through the authentication layer, the network layer, some authorization policies that you have set up, etcetera. So every time you make a REST call, if you make this twice for every for every inference request, your total around round trip latency goes up. And finally, an operational overhead. Now you're maintaining two services instead of one. So whenever this service refreshes, you need to ensure that the latest data becomes available to the inference service, etcetera. So all of these overheads that add up, all of that is now eliminated with an with because of the integration that we have built between your real time features and your model server. And we'll look at it in a in a minute in demo as well. So the way the feature works is when you define you say you've set up your feature store, an online feature store backed by Postgres. And now you when you deploy an imperial time inference service, you pass in which are the feature views this model requires. And by doing that, your feature store latency is already under forty thirty milliseconds, and your ML inference is under the already under fifty milliseconds. The round trip latency comes down to under five hundred hundred milliseconds for any most transactions. And this is I'm talking p ninety nine. The p fifties are much lower. By doing so, your operational overhead goes down as well. Your natively integrated ML ops suggest your monitor, your explain, your auto capturing of inferences. All of these can and accuracy over time, all of these have to not be set up individually for each of these entities. It's all encapsulated within the model. And finally, you have the same your data platform, which has already been governed. You have your models running along with it with the same security and governance policies that we have that works, that can be automatically extended from the data to the models. With that, let me quickly jump into an Alright. My notebook should be visible. Just zoom in a little bit so that it's more readable. Alright. So let me know if the the text is not visible, somebody in the call. But yeah. So in this demo, what are we going to do is we're going to quickly train a model using the using the feature store. Data from a feature store. Log that to model registry, deploy it as a real time service, but use the features for integration I was just talking about. So I'm gonna skip some of these things in the interest of time, but it's pretty vanilla notebook. I have a feature store that has been set up, which I can show very quickly. Yeah. Here we go. So this is a feature view where, in this example, we are doing a fraud detection example model. We're using a fraud detection model that is using a that's a simple XGBoost model. It's trained on data such as the transaction amount, transaction machine category, just how far is the home location to the where the transaction happened, time since the last transaction, etcetera. Typical features that are used for training these kinds of models. But instead of directly passing this data while training the model, I'm going to use a feature store view so that it remains consistent when I go to production. I'm going to skip this part. I'm going to train the model using these features. And then once my model is ready, I'm going to use my model registry to log this model. Now once the model is logged, I first need a compute pool on which this model service is going to run. So I'm gonna this is a small model, like g boost. I I don't need a GPU for this, so I'm just gonna use a CPU instance and then create that compute pool. And then once I have that, I use the create service command to deploy my service. A couple of things to notice here, which are different from our existing GA product is, firstly, there is a new feature called new new parameter that you can pass in called the feature sources for this function. So that this is where I define where which features we which online feature view to use should the service be using. Once I define this, the advantage of doing this is, traditionally, if I have the model deployed as a service, if I need to get a prediction, I should be passing in all the features that were given during training. So I should be, for example, passing in the transaction amount, the merchant category, the etcetera. But because of the features for integration, what I can do once I have set this up is I can directly just call this using the transaction ID. And to do that, this is how the final signature would look like. So, of course, I cannot make the rest call itself from this notebook because I'm within Snowflake. The rest call needs to be made outside Snowflake, which I'll do so in a second. But here is what the now the rest call would look something like this, where you give the endpoint URL, which would, sorry, which would look the same, your authentication, whatever authentication mode you're choosing. But here is where it gets interesting. Instead of passing in if if I want to score these three records, all I'm passing in is the transaction IDs, if you see here. I'm not passing in the actual transaction amounts or, etcetera. But let's look at it in action. So I've made a quick Streamlit app locally, which can which takes my authentication token and the endpoint URL and hits that with how many transaction IDs I want to test. So let's I'm gonna put in a random transaction ID. Let's say twenty three. Let's say four, forty five, and this and then I can just do it in predictions. It'll immediately give me for all these three transactions, the prediction is zero, which means it's saying it's not a fraudulent transaction. And I can also look at the one the final out the raw response output would look like. And of and this is, of course, a demo, so it's in a a setup on a streamed app on my local. But ideally, this would look something this would live and breathe in your final production application. Cool. That was my demo. We can do q and a now.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003ERachel Roundy:\u003C/b\u003E Awesome. Thank you so much, Avinash Joshi. We'll switch the screen back over. We've got a couple of great questions. Thank you for covering all of that. So much goodness with online ML. You mentioned that online inference in Snowflake has lower latency than other platforms. How does Snowflake achieve that?\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EAvinash Joshi:\u003C/b\u003E So there are multiple things. So depending so as I said, we are better both from traditional for traditional models and open source models. They've done a bunch of work when it comes to how minimizing IO and making sure that the data throughput when it's can be communicating between services is extremely fast. So we use Pyro for traditional models. We have a bunch of optimizations we have done for the VLLM inference engine for LLM models. So overall, what ends up happening is the result of inference, the throughput increases. And if the throughput increases, since Snowflake is a pay per compute model, the faster your workload finishes, the lower is your cost. So they both go and That's\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003ERachel Roundy:\u003C/b\u003E in very cool. And for customers who are already running online inference on Snowflake, what changes do they actually need to make to adopt this integration?\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EAvinash Joshi:\u003C/b\u003E Yeah. That's a good question. So one thing is because online services are already once you're deployed, it's you you cannot really make changes to them. You will have to recreate you already deploy the service. And while redeploying the service, the only thing you need to add is that one parameter that I showed during my demo, wherein the create service call, you just add this new parameter called, and and then that's it. We're done. So you just pass in the feature view that you wanted to pass in. Once it so it's just created next time, you can start using it using the indication.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003ERachel Roundy:\u003C/b\u003E Awesome. Last question for you. You mentioned ML in Snowflake is agentic. So how does online ML work with CoCo?\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EAvinash Joshi:\u003C/b\u003E So CoCo lets you so the notebook that you just saw, I made it in, I would say, less than ten minutes using CoCo. So so that's the biggest part of it, which is it gets you to this point very, very quickly. Of course, I know all the features, so it so it's much faster. But the the the best thing that agentic the CoCo does for me is getting me from the data to that one production model very, very quickly And making sure I'm following the I'm following the best practices, which are qualified as skills. I'm using the right features for the right for the right problem, and I'm also using the latest and greatest features. A lot of times, what happens is this integration has not been announced. And if you're trying to build this yourself, you will but CoCo will will will help you identify, hey, this is new integration that you can just use, which makes your life more That's\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003ERachel Roundy:\u003C/b\u003E awesome. Well, thank you so much, Avinash Joshi. We appreciate your time and walking us through all the exciting updates with online ML. And this is a great segue because our next topic is CoCo. So I'm gonna hand this over to our expert, Alan. Alan, over to you.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EAlan Yu:\u003C/b\u003E Hello, everyone. Give me one second to share my screen. Okay. Welcome everyone. Thank you so much for taking time with us to hear about all the new AI updates across Snowflake. Today I'm gonna be talking about Snowflake CoCo. And I am a product manager working on CoCo. And one of the most exciting things about being a PM is getting to talk with all of you and seeing all the amazing things that you're building and using CoCo for. And so talking to end users and talking to admins, there's just so many consistent themes that we're hearing from all of you, and it's just so great to learn from all of you. One thing to highlight is that when we talk with all these different teams across your companies, we always kind of hear consistent things. So for example, finance teams are asking about where are these AI costs coming from, which teams are spending the most. From platform perspective, they want to know whether the governance policies that have been set up, whether those are actually being followed with AI. Security wants to make sure there are controls, and then developers ultimately want to make sure that their context is well understood whenever they are writing code. And so as we see each of these major technology platforms evolve over time, that reaches a point where governance becomes just as important as innovation. And AI is entering that stage right now, and trust is what unlocks scale. And so, you know, Rachel showed this a little bit earlier, but we believe that CoCo lies at that agentic control plane between your models as well as your enterprise data and context. And this is exactly the spot where a lot of our customers are seeing the magic of CoCo CoWork and really seeing how these can help you with your everyday tasks. And so when we think about Snowflake CoCo, at least for today, we're gonna talk about this in three pillars, and we'll go into detail for each one of them. One is around cost. Cost, cost, cost. As you know that we spent a good portion of of AI pulls today to go through all the new cost controls. Well, we wanna spend even more time talking about this because this is something that you always talk to me about whenever you talk about CoCo. In addition to that, I wanna spend a little bit of time around context. And then finally, I want to talk about a lot of things that we're doing to make sure that CoCo is exactly wherever you are so that it can help you with whatever task you are trying to complete. And so let's start off with cost. AI governance begins with cost. And we know that across CIO to finance to developers, we need the cost to be predictable. We need clear attribution and make sure that this adoption scales in enterprise. And so to start off, just like we talk about cost governance across Snowflake, those pillars around visibility, optimization, control, all still apply even into the AI world. And we'll go into each of these into detail. From a visibility perspective, you want to make sure that every credit that you spend, you're able to attribute. And so with the recent announcements that Pranav touched on, you'll be able to see things like near real time spend tracking for your AI usage. You'll be able to set up admin notifications to know when how people are spending and using CoCo. And then you'll also be able to see these things in the cost management UI in Snowsight. And so as you set up these per user quotas, you should be able to see those directly in the tools that you're already familiar with. Next, from an optimization perspective, another thing that you have asked for is being able to set default model settings in different surfaces. We're super excited to announce that with agent settings, this allows you to set a default model at the account that will then be respected for different users. There's a lot more things that will be coming with agent settings, and so this is something to just keep in mind. But we know that this is a top request that whether you want to set to a specific model, you now have that flexibility, which will be available this week. In addition to that, we also want you to feel reassured that us on the Snowflake side are doing everything that we can to make sure that there is smart optimizations wherever possible. So we are running a couple experiments to improve model routing where for certain tasks, things like fix it for SQL, being able to recognize that you can use a lower cost model and default to that while still maintain the efficacy. So we are running these experiments right now and hopefully can get this all to you very soon. From a control perspective, you know, one thing that you're able to do with CoCo was be able to set daily limits. Now with quotas PUPR, just to really emphasize emphasize this, you can now set monthly quotas. This is huge because we've heard from you that you may not be using CoCo every single day, but when you do use CoCo, you want to go deep. So being able to set a monthly budget and not just run up against the daily limit is the flexibility that you've been looking for, and we are excited to announce that with Quota's PUPR. In addition to that, you also want to stop overspend of those quota limits and thus Pranup touched on out of the box enforcements while also receiving notifications. And so as you can see, as we talk about cost governance, these things around visibility, optimization, and control is exactly how you should think about this as you are lighting up AI experiences across your company. In addition to the announcements around those three pillars, there's also just general capabilities that with CoCo you're able to do. So just naturally with natural language asking CoCo in whatever surface that you're in to ask about your costs. Also being able to, you know, promote the CU that are working with visualizations. If you see an anomaly, you know, to click on it and CoCo will immediately be able to diagnose that anomaly detection and give you some kind of explanation for what's going on. And we've called this a snap and ask, for example, as well as redesigning key pages inside of SnowSight so that you can have even more visibility. Nice. So going back to those three pillars I was talking about, one was obviously cost, which we spent a lot of time on and we will continue to invest in. Another one is around context. And the more context there is, the more powerful that CoCo can be for helping you get your job done. From that perspective, you know, we know that there's a big tax around setting up that context. And with CoCo, you can feel reassured that when you log into CoCo in CLI, in Snowsight, in desktop, you'll be able to when you start your session, context is loaded right away. So your databases and schemas, your active governance policies, your semantic models and relationships, and recent pipeline changes, you can just get started right away and get started and ask your questions and get to work. And one thing that's also super important with setting context is giving you the ability to manage skills. And I know for many of you, you are building skills locally and you've thought of different ways to implement it through Git. And we're happy to announce that we are actually providing a first party way for you to store these skills and share these skills within your organization with skills and plugins sharing, which will be PUPR soon. This gives you the ability to be able to once people have tested and found that a skill is useful, they could then have an admin certify that skill and roll it out across the organization so they can all use that same skill. This is a skill that could apply across all surfaces, across CLI, across no site, and across desktop, and soon to be as well as CoWork that allows you to recognize the high quality skills and easily share it out and also update various versions. And so we've heard that this is something that you've been looking for as a first party way to leverage skills and super excited to announce that this functionality will be coming soon to you in all in all accounts. From a work everywhere perspective and making sure that we meet you where you are, we have a super exciting announcement that we want to talk with all of you, which is CoCo desktop is now generally available today. Super exciting for us to have a desktop app that you can work locally and recognize your local files and terminals, still have your native Snowflake context with super easy setup. You set up your first connection Snowflake and everything will just be available to you. And this also is a full stack AI IDE that allows you to manage your MCP connectors, your plug ins, and just have one place to be able to run run all your work. You can see in the video on the left that this is from a very basic prompt of building an app. We'll guide you to build internal apps And we'll not only help you create the app, but also help you deploy it so that you can share it across your organization. But that's not it. That that's not all in terms of CoCo desktop capabilities. So one key feature is that a lot of our customers, when they tried out CoCo desktop, actually use editor mode. They will have their Snowflake connections. They'll have their SQL editor, their results grid, and CoCo on the right hand side to help them. Desktop can allow you to just use your everyday workflow and work with Snowflake exactly the the way you're used to. You can bring over your VS Code extensions and just make it this place that you come back to every single day. In addition to that, for those of you that are fully into the agent world, you can actually dive into agent mode and just run multiple complex tasks tasks powered by MCPs. Even as we speak, I'm running about six of these sessions concurrently just for various tasks that I'm doing as a PM. And I myself am a CoCo desktop everyday users. But not only that, you can work with your existing DBT projects, And we have heard customers who have moved to CoCo desktop entirely to manage their DBT projects. You know I mentioned internal app building. And there's just so much more. We are just learning so many things that people are using CoCo desktop. They're not just rolling out to their data teams, they're rolling it across organizations. And so many different personas that you may not expect to work with Snowflake are actually finding value with CoCo Desktop. So we know that many of you have been asking for the GA label for CoCo desktop, and we are excited that we are at that bar today so that you can go and try it out. At the very end, we'll give a QR code that you can go and install it right now. Cool. In addition to other GA announcements, one thing that for those of you that love SnowSight and use SnowSight, we're excited to announce that Cloud Agents in SnowSight is now GA. And so before, for those of that played with CoCo a little bit in Snowsight, maybe back in April, Snowsight had some limitations compared to some of the local capabilities that you can do. Things like running bash shell commands, running web search, Python execution. And now all these things are now available to you in Snowsight. So this gives you even more capabilities in Snowsight for your everyday tasks. And this also lights up additional scenarios for us to provide even more agented experiences in Snowsight that you'll just be able to get for free. But, yeah, there is something that's super exciting for us as we continue to invest into Cloud Agents. In addition, we are also excited that in private preview, we have a CoCo mobile app that as you run these tasks in Snowsight, you'll also be able to if you have some kind of you run some kind of long running task, you go off to lunch, and you want CoCo needs something in order to proceed with the mobile app, you can now continue that action and then come back to your desk with your task complete. So this is something that we're excited to announce as private preview today, and definitely go ask so that you can go and try this out. In addition to surfaces on desktop, on mobile, we want to make sure that you also get immediate improvements in Snowsight. And so one of the announcements in Snowsight is that you had a limitation where you could only have one chat running at a time. We are now excited to announce that there are multiple concurrent chats that you can now run-in Snowsight. So if you have multiple tasks you're trying to do, just create a new chat for each one and just get prompted whenever the task is complete. You can also see in the screenshot here that you can also expand the right hand side to get full screen CoCo. So you could get the full screen real estate and have focus on whatever task you're trying to complete. And we're doing some quality of life improvements like archiving conversations in case you have many chats that you have done over your history of using Kokum. In addition to that, we also have sharing chats capability where you can link chats with your coworkers and share them, hey. I did this investigation into our data. Here's a link to it so that you can go and see what was the analysis that came out of CoCo. And then the last update is around our new home page. And so this is coming soon. A few of you might see it in a few of your accounts early, but this is hope something we're hoping to roll out soon so that as soon as you log in to Snow's site every day, you can go and ask CoCo to help you with whatever task you're trying to do in Snow's site. And so across all of these surfaces, it's all about meeting you where you are and giving you the assurance that you have one governance model that covers all these surfaces so that you don't have to worry about having to do custom things for each surface. And so that's the vision that we are making sure that you have that visibility into managing across all these surfaces so that your teams can be able to run with CoCo. So ultimately, whether you're a builder, whether you're a platform team, whether you're a leadership, we wanna make sure that CoCo can meet the needs that you are trying to do with Snowflake. And just a few takeaways, definitely go and try CoCo desktop. There's a QR code here, but you can also search CoCo desktop and you can go and install it today. Definitely go explore the capabilities that are now available in Snowsight that should light up in all of your accounts or in the process of rolling out. And then also make sure to discover all the new cost controls that are now available to you and something that we'll continue to invest in. Thank you so much for listening in on all the CoCo updates, and we'll hand it back for q and a.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003ERachel Roundy:\u003C/b\u003E Yes. Let's jump over. Awesome. That is so much goodness. I can't even wrap my brain around it, Alan. First of all, like, GA on desktop is a big deal. That's that's really huge news. I'm sure our customers are really excited to use that. I know we personally, we use it all day every day, as you said, all kinds of personas. It is our favorite tool. So really exciting stuff. And Cloud Agents, how cool is that to be able to be able to keep things moving? So, with desktop going GA today, how can users get set up either as an admin or a user?\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EAlan Yu:\u003C/b\u003E Yeah. So I think the first thing to do, and we actually do have some links in our blog, and probably there's some content that we'll give afterwards of just getting started with CoCo desktop. There's a connection option, and I think we'll actually guide you through it, where you set up your Snowflake connection. And then from there, you can be able to select the roles that you already have access to. So when you go to Snowsight, you can see that there's a role picker. In desktop, you can also set the role that you're going to be using desktop in. So if you have account admin access, you can run desktop as an account admin. And if you are running it in one of your other roles, you can run it in those roles as well. So it's really trying to simplify that first day of trying to see value of desktop where you can just connect to your existing Snowflake instance, and it should just pick up on all the permissions that are privileges that you already have.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003ERachel Roundy:\u003C/b\u003E That's so easy. Awesome. I think we also have a great YouTube video by Luke that you can check out. I think we have it linked here. If not, we'll send it out to everyone. Awesome. When you were speaking about costs with CoCo, and Pranav spoke about about cost?\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EAlan Yu:\u003C/b\u003E Yeah. So I think that's one of the beauties of it is that across all of our product teams at Snowflake, we have made an effort to make sure that every product expert for the area, whether it's across security, across cost, across apps, it was to build skills that just come as part of CoCo. And so by asking just a simple question, and you just log in to Snowsight today and go ask like, hey, What does what cost controls are available? What does my spend look like? These are things that CoCo can help you either set up or can even give you answers to the questions that you're looking for. So definitely Awesome. Yeah. That that that's one easy way to get started.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003ERachel Roundy:\u003C/b\u003E And jump in, Pranav. It looks like you've got something to add there.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EPranav Pokharel:\u003C/b\u003E Yeah. I was just gonna say and then for cost specifically to Alan's point, I think Alan had a slide up there that talked about the cost intelligence skills. So you'll you'll notice that when you ask over questions and it's going through the prompts, it's gonna look at that skill and it's gonna that skill basically allows us to like, it's like a semantic definition of what are the relevant and tables and views to look at to answer for your request questions. And so it already has a set up, like, golden set of questions, and we kinda learn as you're gonna asking those questions to CoCo and the the the skill, and I will continue to kinda, like, iterate on that. So that's a very powerful tool available to you.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003ERachel Roundy:\u003C/b\u003E That's awesome. Okay. We have a question here. How does CoCo Mobile work if IP filtering is set up on the account?\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EAlan Yu:\u003C/b\u003E Yeah. So I think we may need to go a little bit more details into that. But ultimately, we wanna make sure that this follows all the managed settings that IT admin has set up. So as long as, you know, admins feel like they have the controls to set up CoCo Mobile, we'll make sure that those are available to the admin to respect those things. But I'm sure there's some follow-up that we can do there to give you more details.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003ERachel Roundy:\u003C/b\u003E Awesome. Well, thank you so much, Alan, and thank you for all the great questions. Everyone in the audience, what great updates on CoCo. We really appreciate your time today. Thank you. All right. And and last but not least, everyone, we have some really amazing updates on AI function. So I would love to introduce you to Jessie Felix Felix. Take it away, Jessie Felix.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EJessie Felix:\u003C/b\u003E Hello, everybody. I'm the product manager for AI functions. So AI functions, for those of you who may not know, is the the transformation layer for how AI engineers process all unstructured data. Now a big thing today is cost optimization. And based on many of my customer conversations lately, I think it's fair to assume that the age of token maxing is behind us. And I think a big trend that I now see is how our customers looking to they're looking to optimize AI workflows in advance to get the best quality ratio before going to production. So like earlier this year, a couple months ago even, I would see like many AI engineers working and building AI data pipelines for unstructured data that defaulted to using the most powerful and oftentimes the most expensive model for every single task. And this approach definitely accelerates prototyping, but what a lot of customers found out when they're trying to go to production on big volumes of data, which we can easily scale to, was that they needed to rethink their strategy to how to like better optimize for cost and making sure that they can deliver for the AI use cases, but in a more cost effective way. So to help with that process, we built the Cortex AI function studio. So the Cortex AI function studio is a skill that is currently built into Cortex, CoCo. And it helps teams, create, evaluate and optimize essentially production ready, custom AI functions without needing any expertise in prompt engineering or model tuning. So whether you're looking to build your custom classifier, whether you're looking to extract different fields from contracts or classify support tickets or summarizing sales call, getting sentiment to analyzing images. The way that you can do this within AI function studio, it's very easy. It's multimodal by default and you can really optimize any type of AI tasks today. And the way that it works, it's pretty simple I would say. Like within CoCo and natural language, you would describe the task, then you can evaluate, against data that you may have, but also there is mechanisms within the skill where we generate synthetic data to help you evaluate different metrics, whether it's exact match or whether using LLM as a judge. And then the AA function studio, after evaluation, you can choose to then optimize a function across different prompts, across different models. So instead of like stitching together an evaluation harness and different prompt experiments, That entire life cycle is entirely automated inside of Snowflake and it's built right in CoCo. And a key thing which makes AI function studio I think unique and different is that it's really optimized for your data. So it's not a generic benchmark that we're optimizing for. And one of the things that we observe across customers is that by optimizing for your specific business use case, customers are building, custom AI functions that are not only more cost effective, but they're observing actually better quality than sometimes even the leading models can deliver with zero shot, prompting, for example. And it really is a game changer for customers, especially customers that are not investing in this evaluation harnesses that are not too sure where to start and how to optimize an AI task on unstructured data. The a function studio makes it really easy. And instead of like investing cycles, days of prompt tuning and model shopping, like all of that process is essentially compressed into a few minutes. And it's really simple. So you get better AI quality, lower cost, and a much faster, path to production that is cost effective. So the big takeaway for me to you today is essentially what you see here, which is don't buy more model than you need. Now I don't have time for a full time demo because running the evaluation and the optimization takes some time. So, but I still wanted to kind of like take a snip and make and give you like a concrete like real example. So what you're looking at here essentially is the results from a custom made function that we built for key information extraction. And we're processing different product reviews, specifically food truck reviews, and we're extracting things like name of food truck, what dishes that they serve, issues that customers raise, etcetera. And a key thing in choosing the function that we're going to go to production with is our goal is to at least achieve an eighty percent accuracy before we're comfortable putting this into production. And what we see here essentially is that a function studio evaluated different prompts and models against the quality target that we have. And then on the bottom, you can see the fully loaded inference costs for each of these options. So the fully loaded cost looking at total like a blend between input and output token cost for the use case that we're implementing. Now by default, I use, and many customers use the largest model. In this case we're showing Claude Opus. And as you would expect, actually it's not surprising, it does in fact deliver the highest quality. But also unsurprisingly, it's also the most expensive model. So at a blended cost of over eight, dollars per one million tokens across both output and input, it is by far the most expensive option. Now on the other side of the spectrum, we also evaluated a smaller model. So in this case, we evaluated GPT five nano. And what we see here is that even though using the a function studio, we were able to optimize the task of key information extraction from going from a baseline of like fifty percent with a very naive prompt to over seventy percent, using LMS a judge for scoring the accuracy. There's a huge improvement, but it's still below the threshold that we're looking to achieve. So it doesn't hit our production threshold, but at the same time showing the, the boost that you get from using the AA function studio on quality. Now the interesting part here is the middle, right? So we have Claude Haiku, which is essentially, we were able to optimize Haiku to essentially deliver for this specific task that we're optimizing. Very similar quality to the largest model, but at eighty percent lower cost. And not only is it eighty percent lower cost, but it is a more lightweight model, which means that it'll have higher throughput, it'll, fuel faster and we'll be able to scale it across, more records, than we would the largest model. So that is really in a nutshell effectively the promise of the AI function studio and the work that we're doing here to help customers optimize their unstructured data pipelines, is it helps you to stop buying more models than you actually need. And the way we do this is by automatically helping you find the right model that meets your quality bar. That is all I have for you.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003ERachel Roundy:\u003C/b\u003E Amazing. I mean, that's a ton. That's fantastic, Jessie Felix. Thank you so much for for walking us through all of that. We have some questions because this is a really great topic. So AI function studio automatically evaluates different models and prompts. What criteria is it optimizing for and how much control does the developer have over that process?\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EJessie Felix:\u003C/b\u003E There's actually a good amount of control that you have with the a function studio going through CoCo. So number one is in terms of the optimization methods, it's using the a Kepa optimization technique. But in terms of like the the metrics that are used and the evaluation that is used, you control that. So you can choose to use, LMS a judge. It is built in to be smart enough to say based on the on the task that you're asking to do, it'll suggest different evaluation metrics. But the user can always guide the model to use a different evaluation metric.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003ERachel Roundy:\u003C/b\u003E Okay. Awesome. This is a little tricky. I'm curious what your answer is. Does optimizing across multiple models increase cost for the optimization step itself?\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EJessie Felix:\u003C/b\u003E Well, the the short answer is yes. So and but the longer answer is that there's no additional cost for using the AA function studio. But during the optimization step, we're effectively running inference on the underlying models that you're looking to test. So for example, if you want to test three models from the Anthropic model family with three models from the OpenAI model family. We would effectively run inference across those models so that we can test different prompts iterations. And those tokens will generate cost, but it is fairly limited. So we don't produce a significant amount of cost, but just enough so that we're able to do an evaluation and then be able to analyze the quality for the outputs.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003ERachel Roundy:\u003C/b\u003E Awesome. This next question, as new models become available, can existing AI functions be reoptimized without rebuilding them from scratch?\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EJessie Felix:\u003C/b\u003E The quick answer is yes.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003ERachel Roundy:\u003C/b\u003E Okay. You can add a couple sentences of context if you'd like.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EJessie Felix:\u003C/b\u003E Yeah. I mean, this is actually I would say like the this is a big use case that is becoming more and more prominent. Like the speed of development of different models is accelerating and customers have asked like, do how do I know when's the right time to move to to a different type of model, so I can upgrade or maybe evaluate something else? And the AI function studio is a direct answer to help customers in that transition phase.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003ERachel Roundy:\u003C/b\u003E Awesome. And the last question, how do folks get started with AI function studio?\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EJessie Felix:\u003C/b\u003E So it's as simple as you would with any, skill. So when you go to the, when you go to CoCo, it's forward slash, AI function studio and it will pop up as a skill and it will and then you will it will guide you to specify your task and then going through the steps to optimize for your function.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003ERachel Roundy:\u003C/b\u003E Amazing. Great. Well, thank you so much, Jessie Felix, for the great updates on AI function studio. What what a great way to optimize. Thank you so much. All right, everyone. Well, with that, we are going to wrap today's AI Pulse. Thank you so much for joining us, for hanging out, for all of the great questions. Any questions that we didn't get to live, we can answer in the tool. And please do remember to check out the attached resources that we linked in the webinar platform. Lots of goodness about all the features we shared today, as well as some of the features that weren't in the deep dives but are still really relevant. 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