Seeing Clearly into Your AI/ML Black Box

Are you running AI or ML on Kubernetes? Is everything really working… or is it just running while silently failing behind the scenes?

AI and ML pipelines are being increasingly deployed in cloud-native environments, and people are paying a lot of attention to the mechanics of running them. But what comes after getting your huge training workloads running and your model queries properly load balanced? There’s a whole class of operational challenges that’s not getting talked about very much: how do you know that everything is really working correctly?

Join us for a look into AI observability! In this session, we’ll talk about the metrics that are usually available and what they really mean, what you need to know to use them to spot problems, and how you can use modern cloud-native tooling for a clean AI-centric metrics pipeline. We’ll wrap up with a demo showing these techniques live in the wild so that attendees can walk away with useful, practical insights into the brave new AI world!