MLOps is hard, because there’s so many “things” that you might want to integrate and connect with: A/B testing, feature stores, model registries, data catalogs, lineage systems, python dependencies, machine learning libraries, LLM APIs, orchestration systems, online vs offline systems, speculative business ideas, etc. In this talk I’ll cover five lessons that I learned while building out the self-service MLOps platform for over 100 data scientists at Stitch Fix. This talk is for anyone building their own, or buying it all off the shelf. Either way you’re still going to want everything to fit cohesively together, i.e. as a platform, and learning what to avoid/focus on will increase your ROI on MLOps initiatives.
