Over 400 million people in 183 markets open their Spotify app at least once a month to see a brand new set of recommendations from a pool of 85+ million songs and podcasts. Those recommendations on the home page, in your playlist creation screen, from your search bar, and in your Discover Weekly are driven from Machine Learning models powered by a complex set of tools working in unison with the sole purpose of building a more intelligent Spotify product.
In this talk, Josh Baer – product lead for Spotify's Machine Learning Platform – will walk you through the lifecycle of a song recommendation and the complex software that orchestrates the various backend, data and ML specialized libraries built to make the lives of Spotify engineers easier. He'll describe the challenges of creating a Platform that trains hundreds of models daily and serves millions of predictions per second for 50+ teams building ML for Spotify, and discuss why there are so many companies building similar tooling.
Josh Baer
Spotify's Machine Learning Platform Lead
@j6aer
Josh leads ML Platform at Spotify, growing an organization of product, design and engineering hyperfocused on increasing the productivity of ML practitioners. During his 9 years at Spotify, he has built data products as an engineer and product leader. He holds a MS in Computer Science from NYU and a BS in Philosophy/CS from the University of Pittsburgh. He's spent the pandemic on the Brooklyn waterfront with his partner, cat, and 14-month old daughter "Kiki".
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