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Waterlink Atrium - All

Tuesday May 19th, 10:15-11:00

Operationalizing ML at scale using Scala and Akka

About this Session

With one of the largest movie and TV catalogs in streaming, it's imperative that our recommendation system matches the right content to the right user in real-time. As our content library and user base evolves, it's also critical that we're able to rapidly iterate and improve user experience through experimentation. With the unique advantages of Scala, Akka, gRPC and ScyllaDB, we've been building a low-latency, scalable machine learning platform with a fraction of the staff it takes to build/maintain similar platforms at Uber, AirBnB, and Netflix.


Chang She

Chang She is the VP of Engineering at Tubi TV where he focuses on building out infrastructure for operationalizing machine learning. A former financial quant, Chang is passionate about making data tools to enable people and organizations more productive and make better decisions. Don't hold this against him but Chang was also the second major contributor to the pandas library that made Python more popular. He holds a B.Sc. and M.Eng. in Computer Science from the Massachusetts Institute of Technology.