Today Amazon SageMaker announced the support of Grid search for automatic model tuning, providing users with an additional strategy to find the best hyperparameter configuration for your model.
Amazon SageMaker automatic model tuning finds the best version of a model by running many training jobs on your dataset using a range of hyperparameters that you specify. Then it chooses the hyperparameter values that result in a model that performs the best, as measured by a metric of your choice.
To find the best hyperparameters values for your model, Amazon SageMaker automatic model tuning supports multiple strategies, including Bayesian (default), Random search, and Hyperband.
Grid search
Grid search exhaustively explores the configurations in the grid of hyperparameters that you define, which allows you to get insights into the most promising hyperparameter configurations in your grid and deterministically reproduce your results across different tuning runs. Grid search gives you more confidence that the entire hyper parameter search

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