Taking a model from research to production is hard — and keeping it there is even harder! As more machine learning models are deployed into production, it is imperative to have tools to monitor, troubleshoot, and explain model decisions. Join Amber …
Workshop: Bring Your Models to Production with Ray Serve
In this workshop, we will walk through a step-by-step guide on how to deploy an ML application with Ray Serve. Compared to building your own model servers with Flask and FastAPI, Ray Serve facilitates seamless building and scaling to multiple models …
Fireside Chat: Is ML a Subset or a Superset of Programming?
Join Mike and Martin in this fireside chat where they’ll discuss whether ML should be considered a subset or a superset of programming. ML can be considered a specialized subset of programming, which introduces unique requirements on the process of …
Lakehouse: A New Class of Platforms for Data and AI Workloads
In this talk, Matei will present the role of the Lakehouse as an open data platform for operational ML use cases. He’ll discuss the ecosystem of data tooling that is commonly used to support ML use cases on the Lakehouse, including Delta Lake, …
Engineering for Applied ML
Applied ML consists of ML algorithms at its core and engineering systems around it. For over a decade as an applied ML practitioner, I have built a number of such engineering systems to help unlock the full potential of ML in a variety of problem …
