Snowflake Summit 2023: Highlights and Enterprise Priorities – Torsten Grabs, Snowflake

Hello, I'm Amanda Ani with Textron Group. Excited to be here today with Torsten Grabs. He is the Senior director and product manager of Snowflake.

How are you doing today? I'm doing fantastic, Amanda. Thanks for having me on.

Wonderful. Glad to have you here. And the big topic of the day, of course, is the Snowflake Summit 2023.

You've had some big announcements so far, and let's start out by just sharing with our audience a little bit about Snowflake and the services you provide, and then go into some of those announcements. Yeah. So, uh, at Snowflake, our vision is to remove silos and, uh, to mobilize everyone's data.

And, uh, AI obviously is a big topic for us these days, also at our summit conference, uh, right now. And it's, uh, mobilizing everyone at really warp speed and it's accelerating our, our vision here, uh, at, at at Snowflake. Some of the kinda key announcements that we made here right now at, at at summit, uh, for me, we're particularly in the compute space with S Park Container Services that really expands snowflake's compute infrastructure dramatically.

It allows you to now run a variety of different workloads natively on Snowflake, uh, compute. And these workloads that we now enable through s Park container services, they include full stack applications, also, uh, the secure hosting of large language models for generative ai and more broadly, uh, a lot of advancements for data science and ma machine learning practitioners. Through that announcement, uh, as well, we are also joined, uh, for that announcement with a number of partners that we are very excited about.

Uh, some of them are, for example, Alteryx, but also astronomer, DataIQ PS, and Media and, and, and SaaS. And they all are, uh, using s Park container services already to provide customers with more secure, easy and govern access to the enterprise data that, uh, customers have, um, and in in Snowflake. Yeah, Fantastic.

Can you share a little bit more about Snow Park and some of those partnerships? Some of those partnerships? Yeah.

So for instance, um, what, what, what you have the ability with Snow Park Container Services here is to literally run these full stack applications from those partners within the security context of your Snowflake account. And maybe just picking one example here, uh, with, with with Hicks, um, you now have a, a state-of-the-art notebook environment that completely runs end-to-end in your Snowflake account. And all compute that you do either in the Notebook UI itself or that you maybe do through s park data frame operations on, uh, your Snowflake warehouse, all of that stays within, uh, your organization's security perimeter.

And you don't have to worry about, Hey, when I do this data processing, is my data leaving Snowflake? And how should I think about that from a security and governance perspective? Right.

Also, there are performance benefits of just essentially bringing all that compute that sits, uh, in the notebook to bring that exactly where you also, uh, work with your data in, into Snowflake. So tho those are some of the, the other, um, um, benefits, right. What is some of the major impacts that you think this is gonna have on the enterprise in the next couple years?

In the next couple of years, I think it's, uh, it's gonna tremendously accelerate and enable generative AI and large language model use cases. So some of the other partnerships that we are very excited about are with, uh, Nvidia, it's providing the, the G P U compute backbone, uh, in Snow Park container services for a lot of these generative AI and large language model, uh, use cases that typically run on, uh, G P U backed, uh, machines. And in addition to that, I think the, one of the key concerns around generative AI and large language models is about, uh, sensitive data in the enterprise.

And how do you orchestrate that with secure processing in the generative AI in large language model space? How do you make sure that none of that sensitive information is, is disclosed to, to, to someone who shouldn't have access, uh, to that. And with Snow Park Container services, you can really bring that generative AI large language model compute to where you have your data and where you govern your data, and then you'll rest assured that e even if you're interacting with a large language model, none of that sensitive data is getting de disclosed in ways that you don't want to.

And I think that's gonna enable a lot of really, really interesting use cases, uh, for us and for our customers and for the industry more broadly. Um, the, maybe the more obvious ones are around all the productivity enhancements that we are seeing from generative AI in large language models right now. So think about the coding companions that you have with the likes, uh, of co-pilot or on the a w s side with, uh, code whisper that, uh, that they can mean substantial productivity gains for builders and for developers.

All of that is enabled by generative AI technology. Now, these, these same productivity en enhancements, you'll see them show up in, uh, snowflake, uh, as well. And they're enabled through that, uh, uh, through the, the processing infrastructure that Snow Park Container services, uh, enables.

In addition to that, besides these, these, these productivity gains, there's also an ability for us just to become much, much smarter about the data that we have or the data that an enterprise owns. By enabling large language model and generative AI on that data, you can build up a much deeper, much better understanding of your organization's data. And obviously you can only do that if you actually feel secure and confident about, uh, privacy for that, that, that, that processing that, uh, you want to do with generative AI and large language models.

And I think that's gonna be the other kind of big, big aspect that's gonna drive innovation and additional insights, um, for enterprise enterprises by unlocking the power of generative AI and large language models over enterprise data. Generative AI is the big topic of the year, and it seems to be advancing really quickly. I know there are some concerns around generative AI and getting some regulations on it.

Um, what is your view on, um, enterprises as they try to harness this AI technology, how do they make sure to keep it safe and secure and, um, uh, deal with the ethical standards of ai? Yeah, that's a great question, and I think it's, it's top of mind for, um, for everyone. Um, well, we talked about security and governance already, uh, a little bit here.

Um, what I would suggest organizations do on, on, on that front is, um, everybody can today already orchestrate with, uh, with cloud hosted, uh, generative AI and M L M services. I would encourage organizations to really scrutinize, um, the, the security situation when, when they do that with enterprise data to, uh, in, in Snowflake to orchestrate with an external service, you wanna understand what data you can actually send over, uh, given your privacy obligations, right? And, uh, for some data, uh, or, and a lot of data that may actually mean that you don't wanna send it into these, uh, these places, but you'd rather keep it in Snowflake and then use the l l m capabilities from our partners to do the processing in Snowflake where you can maintain your, your privacy, um, uh, posture, right?

So that's, that's, that's one aspect. Um, the other one that, that, that, um, that is talked about quite a bit is, um, is it gonna displace employees completely, uh, will part of the workforce be, uh, out of jobs because of generative AI and large language models? And there's, there's certainly, uh, some aspect to that with all of these productivity gains that generative AI and large language models will unlock.

But generally speaking, um, the, one of the big problems is that, uh, that generative AI still can lead to wrong results for hallucinations as it is being called. So I typically encourage leaders that I speak with to keep humans in the loop so that you have oversight of generative AI and large language models when you have someone who, um, who actually vets the results that are being produced by these automated systems. And by keeping the human in the loop, then you can have the confidence as an organization that you are not blindly trusting a potentially wrong, uh, result.

Maybe that's, that's another kind of key aspect to, to keep in mind. Um, obviously generative AI and large language models are getting better, but it will take us some time until, um, we can like blindly trust, uh, them, and I would caution organizations to probably not do that, um, for the foreseeable future. Um, in addition, in addition to that, I think there's, there's, there's also, um, uh, kind of legal situation that is, uh, evolving in different jurisdictions about what, uh, use of generative AI in large language models is permissible.

And we are obviously enable customers to make, uh, well-informed choices there. So for instance, if something is not allowed in a particular jurisdiction, uh, we'll make sure that the product capabilities, um, give them the, the option to opt out from, from those capabilities. Absolutely.

And that key point you raised, the human element as I speak with thought leadership, um, leaders across the space, they all say keep the humans in the loop. That is key. It is simply just a enhancing tool at this moment in time.

Yes. Um, so as you are working with these different partners and different businesses, what are the key issues or struggles you're seeing them have as they try to harness this AI technology, um, and as they try to better analyze their data for digital transformation purposes? I think it's one of the issues is the one that we already touched on is how can I make sure that, um, the, that I have access to, uh, the, the, the most promising data that I want to, that I want to process.

And, uh, a lot of that data is, is actually enterprise data. And I think the key, the key challenge here is that we have made a lot of advances with large language models being trained over publicly, uh, available data. And the results from that are very promising, but I, but I do expect even better results when we can actually access, uh, for these large language models, the data from the enterprise, and then also create specific model instances that are optimized or even fine tuned about specific data sets for specific organizations for specific use cases.

And I think that will unlock the next level of value from generative AI when we can do that, uh, securely. Because then, um, these, these, these large models are very, very broad. Um, and they can, because of that can, can answer to a lot of use cases, but that may not necessarily be required for an organization that is just interested in solving a very, very specific use case.

And then it might actually be better from a result quality perspective to highly optimize a large language model on the specific data set from that organization. Um, it may also give them, uh, cost benefits because a narrow model will not require as much compute resources. So if you actually have a highly optimized but narrow model for a specific use case, you may be able to run it with very, very high accuracy at much, much lower cost.

Right. And those are some of the use cases that we are en enabling through our partners. Like Nvidia, for example, is very focused on that particular use case where you start with a set of base models, uh, that you then using frameworks from Nvidia train and optimize over enterprise data for a very specific use case.

Uh, digital transformation is of course another big topic and most companies are digitally transforming in one way or another. And what is your advice to them as far as if they're behind the mark on this and they're just getting into this digital digital transformation phase, where do they start? What is step one?

Um, I think maybe, maybe the word of encouragement here is that, uh, almost everyone is still kind of, uh, starting on the generative AI and large language model, uh, uh, wave, at least in terms of the, the enterprises that are embracing that for their sensitive, uh, data. I think we're just still in a starting position here. And that levels the playing field, I would say to, to a degree.

So I would en I would encourage organizations to get started now. Um, now is, is is the time to essentially wrap your head around it? And that also may put you in a position where, uh, you'll feel better about how you compare to some other players in, uh, in your industry, right?

Because everybody's now starting from that same, uh, place, um, more or less. Um, also I think it's gonna be interesting to see what are, what is the impact of generative AI in large language models, um, on let's say data science and machine learning in the enterprise more broadly? I think through the ability of generative AI to, uh, engage with less technical users through natural language and through a more conversational, uh, structure, um, I think you have the ability to also provide value for, uh, different personas in the organization without having going, uh, without having to go through, let's say a data science team or, uh, machine learning engineering, uh, team, which in many organizations, uh, have been very bottlenecked over the last few years.

And, um, by leaning on something that is more self-service for less technical people, I think, uh, generative AI and large language models are, uh, are a big opportunity for, uh, less technical, um, organizations. It'll be interesting to watch the, the future and this technology unfold as it relates to business. So do you have any other key announcements or thoughts that you wanna throw out there about the Snowflake Summit?

Yeah, super excited to be here. Uh, I mean, the, the buzz is great. It's great to meet all, all, all the people.

I've already started, uh, talking with, uh, some of the customers and, uh, plugging their brains about what's going on in, in the industry. So I'm very much, uh, looking forward to meeting more customers, um, and better understanding of, uh, how they're thinking about the, the space and also getting a better sense of how all these announcements are resonating with them and what use cases, uh, they would, uh, put on Snowflake with these new capabilities. So very much looking forward to that.

Great. Well, thank you for taking time to speak with me. I'm excited to hear more news as the summit unfolds.

It's been really great so far. Thank you so much. Thank you.