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Intro
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Game Design is Hard
3
Personalization Problem Formulation
4
Personalization Method 2: Prediction Models
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Personalization Wishlist
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Solution: Reinforcement Learning (RL)
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RL Model Training
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Academic RL Applications
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Production RL Applications for Personalization
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Production RL Challenges
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RL-Bakery
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Real Time Model Serving
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Choose the Right Application
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Designing Actions
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Choosing RL Algorithms
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Hyperparameter Tuning Automation
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Key Takeaways
Description:
Explore the practical application of Deep Reinforcement Learning (RL) in industry through this 26-minute talk from Databricks. Learn how Zynga leverages RL to personalize mobile games for millions of users daily. Discover the challenges and solutions in productionizing Deep RL applications using tools like Spark, MLflow, and TensorFlow. Gain insights on applying cutting-edge AI techniques to real-world scenarios, including tips for training RL agents and overcoming production challenges. Understand how to formulate personalization problems, design actions, choose appropriate RL algorithms, and implement automated hyperparameter tuning. Walk away with key takeaways on harnessing the power of Deep RL for business applications and improving user engagement at scale.

Productionizing Deep Reinforcement Learning with Spark and MLflow

Databricks
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#Computer Science #Machine Learning #Reinforcement Learning #Deep Reinforcement Learning #TensorFlow #Business #Marketing #Digital Marketing #User Engagement #Hyperparameter Tuning #MLFlow
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Intro