Explore how the model-per-customer approach enables contextual predictions by training models on individual behavior rather than population averages.

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Most businesses still rely on a single model trained on population data to predict customer behavior. While this may appear personalized, it often produces generalized outcomes. In this session, we explore the model-per-customer approach and how it enables truly contextual predictions by training models on individual interaction histories. 

We’ll unpack the technical realities of operating millions of models, including real-time learning, cold start challenges, and scalable infrastructure required to support this shift in machine learning systems.