AWS – Amazon SageMaker Automatic Model Tuning now supports up to 10x faster tuning and enables exploring up to 20X more models

Amazon SageMaker Automatic Model Tuning enables you to find the best version of a model by finding the optimal set of hyperparameter configuration for your dataset. Starting today, SageMaker Automatic Model Tuning now supports running up to 100 parallel training jobs for hyperparameter tuning, which gives you a 10X increase of parallel training jobs so you can complete your tuning faster. Additionally, for “Random” search strategy, SageMaker Automatic Model Tuning now supports exploring up to 10,000 hyperparameter configurations, a 20x increase over previous limit of 500, enabling you to improve coverage of search space leading to potentially better predictive performance of your model.

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