Build, train, and deploy machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows
PARTNER
Amazon SageMaker Automatic Model Tuning (also known as hyperparameter tuning or hyperparameter optimization) finds the best version of your machine learning (ML) model by running multiple training jobs on your dataset using your specified algorithm and hyperparameter ranges. It then chooses the hyperparameter values that result in the best performing model, as determined by your chosen metric. You specify an ML model to tune, your objective metric, and the hyperparameters to search, and SageMaker Automatic Model Tuning finds a better version of the model in the most cost-effective way.
Amazon SageMaker Autopilot eliminates the heavy lifting of building ML models. You simply provide a tabular dataset and select the target column to predict, and SageMaker Autopilot will automatically explore different solutions to find the best model. You then can directly deploy the model to production with just one click or iterate on the recommended solutions to further improve the model quality.
