Machine Learning in a product is a double-edged sword. It can make a product more useful but it depends on assumed and strictly defined behavior from users.
Hannes walks through the entirety of their machine learning pipeline, how they implemented it, what the elements are, what the learning looks like, and what tooling looks like.
Hannes maps out what good data hygiene looks like not only from the machine learning perspective down to the software engineering, design, and backend engineering, all the way to the data engineering perspectives.