Move Fast and Don't Break Things -- how to build a data platform that scales with your organization

There are two phases to {ML, LLM, AI} Ops: (1) [Urgent] I need to get my model to production! and (2) [Urgent] I just got a model to production, what do I do? Most platforms make only one of these easy, and ignore the other. This often leads to a tough choice: either quickly ship a brittle, opaque model, or navigate a maze of technical jargon and complex infrastructure to deploy a more robust implementation. In this talk, we tear down this dichotomy and present the open-source library Hamilton that we developed at Stitch Fix, where we built the self-service ML platform for over 100 data scientists/MLEs. We’ll dive into the wide array of challenges we’ve dealt with in getting a model to production and maintaining it, present the ways Hamilton addresses both phases, and connect/compare it to other tools and trends in the industry.