Amazon SageMaker Pipelines

Purpose-built service for machine learning workflows

What is Amazon SageMaker Pipelines?

Amazon SageMaker Pipelines is a serverless workflow orchestration service purpose-built for MLOps and LLMOps automation. You can easily build, execute, and monitor repeatable end-to-end ML workflows with an intuitive drag-and-drop UI or the Python SDK. Amazon SageMaker Pipelines can scale to run tens of thousands of concurrent ML workflows in production.

Benefits of SageMaker Pipelines

Compose, execute, and monitor GenAI workflows

Create and experiment with variations of foundation model workflows with an intuitive drag-and-drop visual interface in Amazon SageMaker Studio. Execute the workflows manually or on a schedule to automatically update your ML models and inference endpoints when new data is available.

Train Abalone model diagram

Lift-and-shift your machine learning code

Reuse any existing ML code and automate its execution in SageMaker Pipelines with a single Python decorator (@step). Execute a chain of Python Notebooks or scripts with the ‘Execute Code’ and ‘Notebook Job’ step types.

choose best models

Audit and debug ML workflow executions

View a detailed history of the workflow structure, performance, and other metadata to audit ML jobs that were run in the past. Dive deep into individual components of the end-to-end workflow to debug job failures, fix them in the visual editor or code, and re-execute the updated Pipeline.

Automatic Tracking of Models