The success of any machine learning (ML) pipeline depends not just on the quality of model used, but also the ability to train and iterate upon this model. One of the key ways to improve an ML model is by choosing better tunable parameters, known as hyperparameters. This is known as hyperparameter optimization (HPO). However, doing this tuning manually can often be cumbersome due to the size of the search space, sometimes involving thousands of training iterations.
This post shows how Amazon SageMaker enables you to not only bring your own model algorithm using script mode, but also use the built-in HPO algorithm. You will learn how to easily output the evaluation metric of choice to Amazon CloudWatch, from which you can extract this metric to guide the automatic HPO algorithm. You can then create an HPO tuning job that orchestrates several training jobs and associated compute resources. Upon completion,

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