Revisit Amazon Web Services re:Invent 2024’s biggest moments and watch keynotes and innovation talks on demand
Amazon SageMaker Pipelines
First purpose-built CI/CD service for machine learning
Amazon SageMaker Pipelines is the first purpose-built, easy-to-use continuous integration and continuous delivery (CI/CD) service for machine learning (ML). With SageMaker Pipelines, you can create, automate, and manage end-to-end ML workflows at scale.
Orchestrating workflows across each step of the machine learning process (e.g. exploring and preparing data, experimenting with different algorithms and parameters, training and tuning models, and deploying models to production) can take months of coding.
Since it is purpose-built for machine learning, SageMaker Pipelines helps you automate different steps of the ML workflow, including data loading, data transformation, training and tuning, and deployment. With SageMaker Pipelines, you can build dozens of ML models a week, manage massive volumes of data, thousands of training experiments, and hundreds of different model versions. You can share and re-use workflows to recreate or optimize models, helping you scale ML throughout your organization.
Key Features
Compose, manage, and reuse ML workflows
Using Amazon SageMaker Pipelines, you can 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.
