This week I spoke with Emmanuel Ameisen, a data scientist and ML engineer currently based at Stripe. Emmanuel also wrote an excellent O’Reilly book called “Building Machine Learning Powered Applications”, a book I find myself often returning to for inspiration and that I was pleased to get the chance to reread in preparation for our discussion.

Emmanuel has previously worked at Insight Data Science where he was involved in mentoring and guiding dozens of data scientists who were working on building their ML portfolio projects. He brings a wealth of experience to the table and I’m really excited to present our conversation to you.

In this clip Emmanuel talks through some common patterns he’s seen when ML practitioners go from no model to their first model, and how tooling can support that process:

Emmanuel’s book is packed with practical and useful examples of how to iterate quickly and deliver something that has value in the real world (as opposed to something that simply performs well in terms of a single metric).

As always, full show notes and links are available on our dedicated podcast page.


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