Amazon SageMaker Pipelines

Purpose-built service for machine learning workflows

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

Standardize FMOps practices across your organization to accelerate model development
Orchestrate ML workflows for data pre-processing, model tuning, and deployment
Share and re-use MLOps system that is tailored to your organizational needs

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.

Train Abalone model diagram

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.

choose best models

Automatic tracking of models

Amazon SageMaker Pipelines logs every step of your workflow, creating an audit trail of model components such as training data, platform configurations, model parameters, and learning gradients. Audit trails can be used to recreate models and help support compliance requirements.
Automatic Tracking of Models