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Using Machine Learning at Scale: A Gaming Industry Experience!

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Using Machine Learning at Scale: A Gaming Industry Experience!

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Games earn more money than movies and music combined. That means a lot of data is generated as well. One of the development considerations for ML Pipeline is that it must be easy to use, maintain, and integrate. However, it doesn’t necessarily have to be developed from scratch. By using well-known libraries/frameworks and choice of efficient tools whenever possible, we can avoid “reinventing the wheel”, making it flexible and extensible.

Games earn more money than movies and music combined. That means a lot of data is generated as well. One of the development considerations for ML Pipeline is that it must be easy to use, maintain, and integrate. However, it doesn’t necessarily have to be developed from scratch. By using well-known libraries/frameworks and choice of efficient tools whenever possible, we can avoid “reinventing the wheel”, making it flexible and extensible.

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Using Machine Learning at Scale: A Gaming Industry Experience!

  1. 1. Using Machine Learning at Scale: A Gaming Industry Experience! Arthur Gola Data Science Manager @ Wildlife Studios Vini Jaiswal Customer Success Engineer @ Databricks
  2. 2. 01 02 03 04 05 2
  3. 3. 01
  4. 4. Wildlife is a blend of excellence in art and creativity with data science and high-end technology
  5. 5. Top 10 2.5 billion 10s of terabytes petabytes60+ games released
  6. 6. 02
  7. 7. Tracking Projects Models Massivelyscalabledatacleansing&transformation ETL / Data Processing Data Science Platform ML Runtime BRONZE SILVER GOLD DELTA BI/Dashboarding Marketing Analysts Product Managers Wildlife Games Credits: Pedro Sereno Galvão kafka connect
  8. 8. 03
  9. 9. ● 60% of our revenue ● 95% of our user base ● purchase, only 5% ● Hardcore casual
  10. 10. Frequency: Price: Discount: Content:
  11. 11. Frequency: Price: Discount: Content:
  12. 12. Frequency: Price: Discount: Content:
  13. 13. Frequency: Price: Discount: Content:
  14. 14. profitable
  15. 15. Reinforcement Learning sequence of offers long-term rewards explore-exploit tradeoff too many dimensions
  16. 16. 04
  17. 17. ● Rating and matchmaking systems ● Advanced experimentation techniques ● LTV models ● Ads monetization system
  18. 18. Feedback Your feedback is important to us. Don’t forget to rate and review the sessions.

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