A production ML system at King predicts the day of a player's next in-app purchase, and a GRU-based model reports improved offline metrics plus a 20% A/B test lift over a rule-based baseline.
Continual Learning in Practice
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abstract
This paper describes a reference architecture for self-maintaining systems that can learn continually, as data arrives. In environments where data evolves, we need architectures that manage Machine Learning (ML) models in production, adapt to shifting data distributions, cope with outliers, retrain when necessary, and adapt to new tasks. This represents continual AutoML or Automatically Adaptive Machine Learning. We describe the challenges and proposes a reference architecture.
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Development of an End-to-end Machine Learning System with Application to In-app Purchases
A production ML system at King predicts the day of a player's next in-app purchase, and a GRU-based model reports improved offline metrics plus a 20% A/B test lift over a rule-based baseline.