A High-Level Approach for Solving MLOps Challenges

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.

Key Takeaways:

Additional Resources

• Link to slides
• Dean's podcast
• [BLOG] 17 Top MLOps Tools You Need to Know
• [PODCAST] Operationalizing Machine Learning with MLOps
• SKILL TRACK] MLOps Fundamentals