At Amazon Web Services (AWS), we offer Amazon SageMaker, a fully managed machine learning (ML) platform that allows you to perform automatic model tuning. Amazon SageMaker Automatic Model Tuning finds the best version of your ML model by running multiple training jobs on your dataset. It uses your specified algorithm and hyperparameter ranges.
SageMaker offers an intelligent version of hyperparameter tuning methods that is based on Bayesian search theory and is designed to find the best model in the shortest time. It starts with a random search but then learns how the model is behaving with respect to hyperparameter values. For more information, read how hyperparameter tuning works in SageMaker.
SageMaker Automatic Model Tuning also supports Hyperband, a new search strategy. Hyperband can find the optimal set of hyperparameters up to three times faster than Bayesian search for large-scale models such as deep neural networks that address computer vision problems.
You can also read how to perform automatic model tuning with SageMaker. You use the SageMaker hyperparameter tuning module with built-in SageMaker algorithms, with custom algorithms, and with SageMaker prebuilt containers. The webpage provides comprehensive self-learning tutorials and exercises to help you learn to perform hyperparameter optimization.
Getting started in SageMaker is easy; all you need to do is create a free AWS account. With AWS Free Tier, you get a two months free trial of SageMaker before you need to start paying.