What I Learned From Attending TWIMLCon 2021

In January, I attended TWIMLcon, a leading MLOps and enterprise ML virtual conference. It focuses on MLOps and how enterprises can overcome the barriers to building ML models and getting them into production. There was a wide range of both technical and case-study sessions curated for ML/AI practitioners. In this long-form blog recap, I will dissect content from the talks that I found most useful from attending the conference.

The post consists of 14 talks that are divided into 3 sections: (1) Case Study, (2) Technology, and (3) Perspectives.

1 — Case Study

1.1 — How Spotify Does ML At Scale

Over 320 million Spotify users in 92 different markets worldwide rely on Spotify’s great recommendations and personalized features. Those users created over 4 billion playlists from a catalog of over 60 million tracks and nearly 2 million podcasts. With the massive inflows of data and complexity of the different pipelines and teams using the data, it’s easy to fall into the trap of tech debt and low productivity. The ML Platform at Spotify was built to address that problem and make all our ML Practitioners productive and happy. Aman Khan and Josh Baer gave a concrete talk that describes the history of the ML Platform at Spotify.