What is Amazon SageMaker Pipelines?
Machine learning (ML) workflows are hard to build because you need to create hundreds of code packages for data preparation, model training, and model deployment and chain them together to run as a sequence of steps. Amazon SageMaker Pipelines is an ML workflow orchestration service for Data Scientists and Developers to manage end-to-end automated ML workflows with the familiar Python interface and an intuitive UI.
Benefits of SageMaker Pipelines
Compose, manage, and reuse ML workflows
Create ML workflows with an easy-to-use Python SDK, and then visualize and manage your workflow using Amazon SageMaker Studio. You can be more efficient and scale faster by storing and reusing the workflow steps you create in SageMaker Pipelines. You can also get started quickly with built-in templates to build, test, register, and deploy models so you can get started with CI/CD in your ML environment quickly.
Choose the best models for deploying into production?
Many customers have hundreds of workflows, each with a different version of the same model. With the SageMaker Pipelines model registry, you can track these versions in a central repository where it is easy to choose the right model for deployment based on your business requirements. You can use SageMaker Studio to browse and discover models, or you can access them through the SageMaker Python SDK.
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.
Bring CI/CD to machine learning
Amazon SageMaker Pipelines brings CI/CD practices to machine learning, such as maintaining parity between development and production environments, version control, on-demand testing, and end-to-end automation, helping you scale ML throughout your organization.
