Productionizing Deep Reinforcement Learning with Spark and MLflow

Deep Reinforcement Learning has driven exciting AI breakthroughs like self-driving cars, beating the best Go players in the world and even winning at StarCraft. How can businesses harness this power for real world applications? Zynga has over 70 million monthly active users for our mobile games. We successfully use RL to personalize our games and increase engagement. This talks about the lessons we’ve learned productionizing Deep RL applications for millions of players per day using tools like Spark, MLflow and TensorFlow. Hear about what works and what doesn’t work when applying cutting edge AI techniques to real world users.

Takeaways:

  • How to apply Deep Reinforcement Learning to solve business problems
  • Understand challenges that arise in applying RL to industry
  • How we use Databricks, Spark and MLflow to productionize RL
  • Tips and Tricks on training RL Agents for real world applications

 
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About Patrick Halina

Zynga

Patrick Halina leads the ML Engineering team at Zynga, where he works on productionalizing ML workflows and developing personalization technology. Prior to Zynga, he worked on the ML Marketing platform at Amazon. He received his undergrad in Computer Engineering and Master's in Statistics at the University of Toronto. He lives in Toronto, Canada.

About Curren Pangler

Zynga

Curren Pangler is a Principal Engineer on Zynga’s Machine Learning Engineering team. He currently builds ML personalization systems that automatically tailor games to individual players. Curren received his Bachelor’s in Engineering Science and a Master’s in Applied Computing from the University of Toronto. He lives in Toronto, and loves snowboarding, board games, sports, and stand-up comedy.