Ethan Rosenthal is a data scientist at Square, has worked as a data consultant, and used to be a scientist scientist with a PhD in Physics from Columbia University. In this fireside chat, Ethan joins Hugo Bowne-Anderson, Outerbounds’ Head of Developer Relations, to discuss the wild west of full stack machine learning and how to make sense of all the feature stores, metric layers, model monitoring, and more with a view to deciphering what mental models, tools, and abstraction layers are most helpful in delivering actual ROI using ML.
After attending, you’ll know about:
- How to think about the full stack of machine learning in a principled way;
- What the most important layers in the ML stack are for data scientists;
- How to separate the wheat from the chaff when thinking about which tools and abstraction layers to adopt for your team;