SageMaker JumpStart now supports automatic tuning

Amazon [SageMaker JumpStart](https://docs.aws.amazon.com/sagemaker/latest/dg/studio-jumpstart.html) now supports model tuning with [Sagemaker Automatic Model Tuning](https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning.html) from its pre-trained model, pre-built solution templates, and example notebooks. This means customers can automatically tune their machine learning models to find the hyperparameter values with highest accuracy within the range customers provide through SageMaker API. SageMaker JumpStart allows customers to fine-tune and deploy a wide variety of pre-trained models across popular ML tasks, as well as a selection of end-to-end solutions that solve common business problems. These features remove the heavy-lifting from each step of the ML process, making it easier to develop high-quality models and reducing time-to-deployment. Customers can access JumpStart via APIs in notebook, and UI in SageMaker Studio with just few clicks. Through the integration with SageMaker Automatic Model Tuning, JumpStart API example notebooks now include a step to find the best version of the model by running training jobs on the provided dataset with multiple hyperparameter configurations. This reduces the time to tune models by automatically searching for the best hyperparameter configuration within the default hyperparameter ranges or the ranges that you specify. To learn more about SageMaker Automated Model Tuning, refer to [documentation](https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-how-it-works.html). To get started with SageMaker JumpStart, check out the [getting started page](https://docs.aws.amazon.com/sagemaker/latest/dg/studio-jumpstart.html) and [API release blog](/blogs/machine-learning/amazon-sagemaker-jumpstart-models-and-algorithms-now-available-via-api/).