New Amazon SageMaker Pipelines integrations with popular third-party tools
Amazon SageMaker Pipelines, the first purpose-built continuous integration and continuous delivery (CI/CD) service for machine learning (ML), is now integrated with popular third-party source code repositories such as GitHub and BitBucket; and software development automation tool - Jenkins. Customers can now leverage the same tools that they use for managing the software development lifecycle for building and deploying ML models as well, eliminating the need to adopt new tools for managing the ML lifecycle and speeding up their ML projects.
Amazon SageMaker Pipelines, the first purpose-built continuous integration and continuous delivery (CI/CD) service for machine learning (ML), is now integrated with popular third-party source code repositories such as GitHub and BitBucket; and software development automation tool - Jenkins. Customers can now leverage the same tools that they use for managing the software development lifecycle for building and deploying ML models as well, eliminating the need to adopt new tools for managing the ML lifecycle and speeding up their ML projects.
Customers can configure their SageMaker Projects to leverage GitHub and BitBucket as their source code repositories and trigger the execution of the SageMaker model building pipeline whenever code is checked into these repositories. They can also configure their projects so that the entire workflow - from the triggering of the SageMaker model building pipeline to the deployment of models to SageMaker inference endpoints - is automated using Jenkins.
To get started, create a new SageMaker Project from the SageMaker Studio or the command-line interface using the new project templates that provide out-of-the box integration with these third party tools. To learn more visit our documentation page.