Amazon SageMaker JumpStart is the machine learning (ML) hub of SageMaker that offers over 350 built-in algorithms, pre-trained models, and pre-built solution templates to help you get started with ML fast. JumpStart provides one-click access to a wide variety of pre-trained models for common ML tasks such as object detection, text classification, summarization, text generation and much more. SageMaker Jumpstart also provides pretrained foundation models like Stability AI’s Stable Diffusion text-to-image model, BLOOM, Cohere’s Generate, Amazon’s AlexaTM and more. You can fine-tune and deploy JumpStart models using the UI in Amazon SageMaker Studio or using the SageMaker Python SDK extension for JumpStart APIs. JumpStart APIs unlock the usage of JumpStart capabilities in your workflows, and integrate with tools such as the model registry that are part of MLOps pipelines and anywhere else you’re interacting with SageMaker via SDK.
This post focuses on how we can implement MLOps with JumpStart models using JumpStart

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