The landscape of ML tooling has become richer and richer over the last few years. New tools are coming out every few weeks that solve that “one nagging problem” in the ML workflow. The result is a jungle of opinionated tooling in the ecosystem that can easily become overwhelming for machine learning engineers and leaders. In this talk we will explore the main challenges that organizations face to scale their ML operations. We will take a look at how the tech giants are solving these problems, explore buy vs. build options, and I’ll share a template of an end-to-end ML workflow stack that you need to take into consideration when building your own ML workflow.