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.
What You'll Learn
- How model-per-customer enables truly contextual predictions
- Technical realities: real-time learning, cold start challenges, scalable infrastructure
- Training on individual behavior rather than population averages