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.

1. Using Machine Learning at
Scale: A Gaming Industry
Experience!
Arthur Gola
Data Science Manager @ Wildlife Studios
Vini Jaiswal
Customer Success Engineer @ Databricks

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5. Wildlife is a blend of excellence in art and creativity with data science and high-end technology

6. Top 10
2.5 billion
10s of terabytes
petabytes60+ games released

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8. 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

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10. ● 60% of our revenue
● 95% of our user base
● purchase, only 5%
● Hardcore casual

11. Frequency:
Price:
Discount:
Content:

12. Frequency:
Price:
Discount:
Content:

13. Frequency:
Price:
Discount:
Content:

14. Frequency:
Price:
Discount:
Content:

30. profitable

31. Reinforcement Learning
sequence of offers long-term rewards
explore-exploit tradeoff
too many dimensions

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33. ● Rating and matchmaking systems
● Advanced experimentation techniques
● LTV models
● Ads monetization system

35. Feedback
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