machine learning models

“I don't know if there are necessarily any sort of general purpose, like this is going to fix everything kind of solutions, but I would say yes, digging into why your model is making certain predictions, even when those predictions are correct, can kind of help…”

“I would argue, though, that even very complicated models can be interpreted using modern techniques.”

“The models that you can create can often tell you what might happen, but they can't always tell you why.”

“If there's a strong enough use case for a different cohort or segment like new users, typically it'll make more sense just to train a new model rather than trying to shoehorn it into a general model.”

“And to get to a point where we can have people that are data scientists that don't even code, that can just build models and be able to deploy those to production. So that's really what we want to get to within Pinterest as well.”

“And so, there's a, that whole pipeline has got like about 50 different machine learning models in itself.”

“In, in some use cases, it's not machine learning per se, or the performance of machine learning models that is the choke point in order to deliver value. It's the ability to actually meet the, meaning the regulatory, the regulation constraints, meaning literal…”

“People talk a lot about data drift in the industry. And that's this idea that like, you know, like language changes over time and you want to know that it's changing and sort of like have your model you know, notice that and update it. But I guess like what I …”

“One of the pieces of functionality we have in CeruleeB is the ability to bring a model right inside the database so that you can run that model. It's a custom trade model or an off-the-shelf model. You can run that model right alongside your data.”