Summary and Quiz
Explore how to run efficient model training on Amazon SageMaker by mastering automated model and hyperparameter selection, distributed training techniques, cost optimization, and production-ready scaling strategies.
Summary
This chapter explains how to run, optimize, and scale training on SageMaker by mapping three training tiers to a unified runtime contract, automating model and hyperparameter selection, and designing production distributed training with cost and observability in mind.
Training approaches and the SageMaker contract
SageMaker exposes a consistent training contract in which data channels map to /opt/ml/input/data/{channel_name}, hyperparameters appear in /opt/ml/input/config/hyperparameters.json, and models must ...