MLCOST-19: Use hyperparameter optimization technologies
Use automatic hyperparameter tuning to run many training jobs and find the best version of your model. Use the algorithm and ranges of hyperparameters that you specify. Use appropriate hyperparameter ranges, as well as metrics that are realistic and meet the business requirements.
Implementation plan
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Use SageMaker automatic model tuning - SageMaker 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.
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