In December 2020, AWS announced the general availability of Amazon SageMaker JumpStart, a capability of Amazon SageMaker that helps you quickly and easily get started with machine learning (ML). In March 2022, we also announced the support for APIs in JumpStart. JumpStart provides one-click fine-tuning and deployment of 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 simpler to develop high-quality models and reducing time to deployment.
In this post, we demonstrate how to run automatic model tuning with JumpStart.
SageMaker automatic model tuning
Traditionally, ML engineers implement a trial and error method to find the right set of hyperparameters. Trial and error involves running multiple jobs sequentially or in parallel while provisioning the resources needed to run the experiment.

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