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

– 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;

And much more! The fireside chat will be followed by an AMA with Ethan and Hugo at slack.outerbounds.co.

00:00 Prelude
06:16 The fireside chat begins!
10:43 Paths into data science, ML, and AI
15:36 Learning to build Recommendation Systems on the job
18:02 Software engineering skills for early career data scientists
21:17 What exactly is ML Engineering?
25:34 Path from IC data scientist to AI engineering manager
27:32 ML at Square — fraud detection, risk, conversation AI, NLP, and more!
31:48 How to measure the success of ML projects
35:30 Feature stores, model monitoring, metrics layers, experiment trackers: what is happening!
44:24 Essential ML libraries for data scientists and MLEs
48:01 What is the full stack of machine learning?
56:11 Software engineering and data science? Two totally different types of programming!
1:01:47 The data science / manager pendulum

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