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). 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 easier to develop high-quality models and reducing time to deployment.
This post is the fourth in a series on using JumpStart for specific ML tasks. In the first post, we showed how to run image classification use cases on JumpStart. In the second post, we demonstrated how to run text classification use cases. In the third post, we ran image segmentation use cases.
In this post, we provide a step-by-step walkthrough on how to deploy pre-trained

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