There’s a pressing need for tools and workflows that meet data scientists where they are. This is also a serious business need: how to enable an organization of data scientists, who aren’t software engineers by training, to build and deploy end-to-end machine learning workflows and applications independently. The new category of tools, frameworks, and processes that fill this need is often called MLOps.

In this edition of Radar Talks, Hugo Bowne-Anderson explains why machine learning applications need special treatment in the first place and why we can’t just fold them into existing DevOps best practices. You’ll also explore what a modern technology stack for streamlined ML processes looks like and discover how you can start applying the stack in practice today.

Recorded on April 5, 2022. See the original event page for resources for further learning or watch recordings of other past events.

The O’Reilly Radar Talks series brings you expert opinions on emerging topics through hour-long interactive events. In conversation with some of the industry’s top minds, you’ll look at the early signals indicating significant trends and new technologies that will shape the future—and discover how to begin forging these technologies now. Share your own predictions and ask your questions while gaining insight into our experts’ unique outlooks for the future.

Please note that slides or supplemental materials are not available for download from this recording. Resources are only provided at the time of the live event.