What is Amazon SageMaker Pipelines?
Amazon SageMaker Pipelines is a purpose-built workflow orchestration service to automate all phases of machine learning (ML) from data pre-processing to model monitoring. With an intuitive UI and Python SDK you can manage repeatable end-to-end ML pipelines at scale. The native integration with multiple AWS services allows you to customize the ML lifecycle based on your MLOps requirements.
Benefits of SageMaker Pipelines
Compose, reuse, and schedule ML workflows
Create ML workflows with an easy-to-use Amazon SageMaker Python SDK, and then visualize them with Amazon SageMaker Studio. You can be more efficient and scale faster by reusing the workflow steps in SageMaker Pipelines. Get started quickly with SageMaker Project templates to build, test, register, and deploy models automatically.
Lift-and-shift your machine learning code
Convert any ML Python code into a repeatable workflow in Amazon SageMaker by adding a single line of code (@step python decorator) or by executing entire notebooks. The Python annotation and the new notebook step provide extensibility by enabling you to incorporate other AWS services for a comprehensive end-to-end ML workflow.