2022 MLops World Conference

June 9th, 2022 , 1:45 PM -2:30 PM EDT

“If you build it, they will come” is a totally bogus way to approach building an ML platform. All the time, teams learn the hard way that the details — justifying the platform, identifying the key components that matter, how it fits into the broader whole, business impact, etc. — are what determines success, not unnecessarily technical specifications or wasting time building a product that will only be irrelevant once it’s done. It’s about fundamentals, and getting those right is hard. Compared to DevOps or data engineering, MLOps is still relatively young as a discipline and best practices are often learned on the fly…so sometimes it pays to buy over build. In this session Gandalf Hernandez, Senior Machine Learning Manager at Spotify, and Aparna Dhinakaran – Chief Product Officer at Arize AI share best practices, war stories, and debate questions such as:

How do you justify building an ML platform internally? What are the key components that matter to your team? And why is ML infrastructure necessarily distinct from software infrastructure?

What You’ll Learn:

The audience will gain insight into what goes on from behind the scenes when building an ML platform at scale. From the specific tools required for machine learning to the rationale behind build versus buy for MLOps tools, audience members can use this talk to help frame their evaluations of various tools or internal efforts to stand up ML infrastructure for their organizations. Audience members will learn common challenges and problems from real-world examples, and how engineers approached those challenges head-on.

June 9th, 2022 , 4:00 PM -4:30 PM PST

June 9th , 5:30 PM -7:30 PM EST