Over the past five years, many new tools have emerged in the field of MLOps, and the existing ones have matured. Yet, there is no clear picture of a canonical stack for productive data science and ML organizations. Through our work with open-source Metaflow, which was started at Netflix in 2017, and Argo Project, we have had an opportunity to work with hundreds of companies at various maturity levels regarding ML infrastructure.

We'll introduce the unique challenges met when incorporating machine learning, data, and the entropy of the real world into existing software stacks. In particular, we'll reason through what needs to change about how we approach building software when experimentation and data become central to it.

We'll talk about the new full stack of machine learning that emerges and how data scientists and machine learning engineers can interact with the stack - with a particular focus on how we went about building it with Metaflow, Argo Workflows, and Argo Events.