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.
You’ll learn what signals to watch for to notice you might have MLOps fatigue. How to define the challenge you’re facing and which questions to ask in order to build a “decision tree” for selecting the best-suited tools for the task. A few examples for using this framework in practice on challenges involving data management and automating training/pipeline tasks
About 2 years ago we faced a crisis in our DevOps consulting company - the market demand was higher than we could supply. The traditional recruiting process depending on CV and artificial credentials was not working. So we came up with an alternative solution, and since then - we are growing exponentially and diversely. In this talk we will show the practical tools we deployed in order to increase our capacity, and we will show how these tools overcome the inherited bias in the process.