An ML system that handles 1,000 queries per second often breaks at 10,000, not because the model is too slow, but because the feature store can't keep up, the training pipeline falls behind, or the data pipeline starts dropping events.

Individual components scale at different rates, and the bottleneck shifts as traffic grows. This chapter covers how to scale each layer of an ML system and, more importantly, how to scale them together.