One of the hardest challenges data teams face today is selecting which tools to use in their workflow. Marketing messages are vague, and you continuously hear of new buzzwords you "just have to have in your stack". There is a constant stream of new tools, open-source and proprietary that make buyer's remorse especially bad. I call it "MLOps Fatigue".
This talk will not discuss a specific MLOps tool, but instead present guidelines and mental models for how to think about the problems you and your team are facing, and how to select the best tools for the task. We will review a few example problems, analyze them, and suggest Open Source solutions for them. We will provide a mental framework that will help tackle future problems you might face and extract the concrete value each tool provides.
Dean has a background combining Machine Learning, Software Engineering, Physics, and design.
He is the CEO and co-founder of DagsHub, a platform for machine learning & AI teams that lets them build better models and manage their project's data, models, experiments, and code effectively—combining popular open-source tools and formats to create a central source of truth for AI projects.
He also hosts the MLOps Podcast, where he speaks with industry experts about getting ML models to production.