The Data Stack Show - The PRQL: Can Machine Learning Be Commoditized?

Episode Date: August 19, 2022

In this bonus episode, Eric and Kostas preview their upcoming live stream episode featuring Willem Pienaar of Tecton and Tristan Zajonc of Continual.

Welcome to the Data Stack Show prequel. We just got done doing one of our favorite things, which is a Data Stack Show live stream. And the topic was ML. And we talked with two brilliant minds, Tristan from Continual AI and Willem from Tekton. And one of the most interesting parts of the chat to meet Costas was the discussion around

what components or even use cases for ML are essentially commoditized or known quantities that are extremely common and have a huge number of patterns around them. So things like churn, you know, any, you know, things like churn recommendations, et cetera. And the discussion around, okay, like these are fairly known quantities where you're changing the inputs and maybe varying the model a little bit, depending on the business use case. I believe Tristan used GitHub's AI code assistant as an example, right? Where it was a huge bet, AI-driven feature, and had a huge payoff and was net new, right? It wasn't optimization.

It was actually adding value. So that was such a fascinating discussion to me in terms of almost a bifurcation of ML into those two categories. What do you think? Yeah, I agree with you. I think, I mean, what I found like super interesting is that I think people, when we're talking about ML, they're still, the first thing that they recall is like, you know, like self-driving cars and I

don't know, AI generated art and like all that stuff, but at the end, this is like, just like, let's say the state of the state of the art right now. And it's probably like, just like the tip of the iceberg. I think the real value in the markets and the economy out there comes from AI that is applied on very structured data and to problems like prediction and optimization in logistics and boring stuff. And what I found super interesting about that is what they said about why these solutions, yeah, they're getting like, let's say the infrastructure might get commoditized, but at the end, its company wants to build, let's say, their own solution

or compete on the solution because it is like a competitive IP that they can. Yeah, that was fascinating. Right. So, yeah, you don't want to have the same trend prediction with all your competitors because at the end, you don't get any heads up. Yeah, that's right. Totally. That was a very interesting part of it. I think it resonates a lot with what like some investors at least say that like it's really difficult.

Like it's like doesn't make sense to create, let's say, products or modes or like try to do that like over data and these kind of like solutions because at the end they end up being services because they need a lot of information for its customer. Which is, yeah, I think both of my experience so far from what I've seen, it's true. Totally agree. And you're definitely

going to want to catch this one if you are working anywhere around ML. We talked about the state of MLOps infrastructure and tons of difficulties there. We talked about the gap between ML Ops infrastructure and tons of difficulties there. We talked about the gap between running ML models to drive insights and actually delivering outputs of models in real time, which is a huge technical gap. And then we also talked about the importance of open source in the ML space. So tons of fascinating stuff from two really smart people.

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