HKML S5E3 - Applied/Production ML/RL for Ads/Recommendations

Applied/Production ML/RL for Ads/Recommendations
Abstract:
Reinforcement Learning has recently gained popularity with the successes in solving difficult high dimensional problems in games, optimization. However, RL is not limited to game-like problem and covers different range of application as well as a broad science field (from ML theory to Optimal control).
So, we highlight key principles of RL and challenges of applying ML/RL for real life/production level for Ads/Recommender. Particularly, we illustrate through a sub-field of RL, called Stochastic Bandit, and his variants, tailored for low latency and high volume inference, typical in online Big Data field.
In addition, we will mention active areas of applied research related to online Ads/Rec such as offline policy methods, causal methods to improve robustness, evaluation.
We highlight processes to productionize Reco algo Ad Optimization, in Japanese context. On the one hand, we describe specificities of Ad data, as well as part of Geo-Spatial data.
On other hand, we illustrate the technical challenges with Online RL/Bandit, from Pytorch side to various data pipelines requirements, different from standard ML.
Finally, we summarize the takeovers and highlight the specificities of Japan
for ML/Data environment with example from Local Industry.
Speaker:
Kevin Noel, Lead of Ad Machine Learning at Mapbox Japan.
Kevin focuses on real time Geo-Spatial Ad targeting/Recommendation.
Previous experience includes Principal in Data Science/Recommendation at Big Data Tech Company (Japan) and in financial industry (quantitative fields at Big Banks).
Slides archived on the HKML website: www.hkml.ai

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Good for machine learning recommendation