Matt, thank you so much for joining us.
So tell us a little bit about the work that Clarifai is doing.
Sure, thanks for having me.
So Clarifai is really the best way to think about Clarifai is
as a full stack AI platform.
So we have all the tools in one place for you to take your idea
and get it into production with AI.
And so that means tools for data labeling, which is always the
first step in trying to customize the AI to recognize
what you care about, managing that data, training models to
then recognize how you care about your data.
And then you really want to evaluate them to see how
accurate they are.
And then all the production scale inference you need to take
that into production and get feedback from your users,
interacting with the AI in real time.
And just today actually, we launched a new capability we're
calling compute orchestration, and it takes all those workloads
and lets you run them anywhere, on any cloud, your own cloud, or
even on-premise.
So what challenges do companies face when taking AI from
prototype to production?
Yeah, there's quite a few.
Some that come to mind are people kind of overestimate how
simple it is when they see on social media, cool, you know,
hack around, maybe OpenAI's model.
Ever since ChatGPT has been a common trend.
And you can prompt these models to do interesting things really easily.
But then you have to think about, are you going to trust
that in front of your users?
Is it reliably giving you back that same result that happened
one time and you posted it on social media and it's going to
happen over and over?
Or is it going to go off the rails and tell your customers
something you don't want them to hear?
Are you going to be able to run that cost effectively?
That's a huge concern with these large models.
And that's one of the reasons why we have this compute
orchestration, to let you run on efficient compute.
And then just thinking about how you're going to go into day two
operations. Getting to production is one thing, but
then how are you going to maintain that with
state-of-the-art, which is evolving at such a rapid rate
right now with all the innovations in AI?
So there's lots of challenges going from that first idea all
the way through production.
And just to jump off that really quick.
So how do companies future proof their projects?
Yeah, we see often people are, you know, building the tools to
build AI rather than just focusing on customizing the AI
for their needs.
And so they're building infrastructure tools, they're
building data-labeling tools, training models.
And it becomes like a whole team just dedicated to trying to stay
up to speed with state-of-the-art rather than
actually innovating with AI to differentiate your business.
So we urge people to think about the long term.
Do you want to be building AI tools? Or do you want want to be
differentiating from your competitors?
So that's one big aspect, just deciding what you want to build
and what you don't.
The other is thinking about the compute long term and your data.
So compute is crucial and doing it in a cost-effective way.
We see this trend of people purchasing more GPUs to bring
that compute back on-premise so that the cloud costs are not,
you know, ballooning.
And that's I think a good strategy for the long term.
And to be hardware agnostic, I think, is really, really
important for the future.
There's great new types of AI-specific processors that
people should be paying attention to.
And then on the data front, data is the lifeblood of AI.
So the first step is always getting your data organized and
thinking about not just the quantity of data you have, but
the quality of it.
Because the better the data, the better the AI is going to
ultimately be.
So it's kind of those big aspects.
What do you want to build, where you're going to run that stuff,
and then the data that powers it.
Thank you so much.
Thank you.