MLCOST-19: Use hyperparameter optimization technologies

Use SageMaker AI automatic model tuning - SageMaker AI automatic model tuning, also known as hyperparameter tuning, finds the optimal model by running many training jobs on your dataset. It uses the algorithm and ranges of hyperparameters that you specify. It then chooses the hyperparameter values that result in a model that performs the best, as measured by a metric that you choose. To create a new hyperparameter optimization (HPO) tuning job for one or more algorithms, you need to define the settings for the tuning job. Create training job definitions for each algorithm being tuned, and configure the resources for the tuning job.

Create a HPO tuning job

Running multiple HPO jobs in parallel on SageMaker AI

Automatic model tuning with SageMaker AI

Large HPO polling examples