Ethan Rosenthal, a data scientist at Square with a PhD in Physics from Columbia University, joins Hugo Bowne-Anderson to dive into the complexities of the full stack of machine learning. From feature stores to model monitoring and experiment tracking, Ethan sheds light on the essential tools, abstraction layers, and mental models that data scientists and ML engineers need to understand in order to deliver real business value.
In this episode Ethan goes over:
- How to think about the full stack of machine learning in a principled way
- Which layers of the ML stack are most important for data scientists
- How to choose the right tools and abstraction layers for your team
This episode was recorded as a fireside chat on 12/14/2022.
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