Session Outline

Rovio’s game teams leverage Beacon, our internal cloud services platform which among other things enables them to leverage data to improve their games. Machine Learning is part of Beacon’s offering. With a few clicks games can start using Reinforcement Learning models with “Personalized rules” which aim to replace complex sets of rules and heuristics that currently are still common across all industries.

Traditionally mobile games have tested different features using A/B testing to pick the best test variant. Using “Personalized rules” there is no need to select a globally optimal variant because our ML models will find the best variant for each individual player.

In this talk the goal is to present this case study putting a special focus on how our MLOps methodology was critical to bridge the gap between experimentation and production.

Key Takeaways

  • From a business point of view, you will learn how Rovio ML product offering provides value in game personalization use cases
  • From a technical point of view, you will learn about the MLOps required to run Reinforcement Learning use cases in production (both contextual bandits and deep reinforcement learning) and the main challenges we faced


Speaker Bio

Ignacio Amaya de la Peña – Lead Machine Learning Engineer | Rovio

Ignacio is a data scientist with data engineering and managerial skills. He approaches projects with a business mentality and always tries to bridge the gap between technical and business leaders from different teams.

Right now, he is working in the mobile gaming sector where he leads the Machine Learning Engineer team at Rovio, which focuses on creating Machine Learning solutions for the games, ads and user acquisition teams.

November 8 @ 17:10
17:10 — 17:40 (30′)

Day 1 | 8 Nov 2022 | MACHINE LEARNING + MLOPS

Ignacio Amaya de la Peña – Lead Machine Learning Engineer | Rovio