Abstract: 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.
Bio: Dean has a background combining physics and computer science. He’s worked on quantum optics and communication, computer vision, software development, and design. He’s currently CEO at DagsHub, where he builds products that enable data scientists to work together and get their models to production, using popular open-source tools.
He’s also the host of the MLOps Podcast, where he speaks with industry experts about ML in production.