Amazon SageMaker Pipelines now supports the testing of machine learning workflows in your local environment
SageMaker Pipelines is a tool that helps you build machine learning pipelines that take advantage of direct SageMaker integration. SageMaker Pipelines now supports creating and testing pipelines in your local machine (e.g. your computer). With this launch, you can test your Sagemaker Pipelines scripts and parameters compatibility locally before running them in on SageMaker in the cloud. Sagemaker Pipelines Local Mode supports the following steps: processing, training, transform, model, condition, and fail. These steps give you the flexibility to define various entities in your machine learning workflow. Using Pipelines local mode, you can quickly and efficiently debug errors in the scripts and pipeline definition. You can seamlessly switch your workflows from local mode to Sagemaker’s managed environment by updating the session.
SageMaker Pipelines is a tool that helps you build machine learning pipelines that take advantage of direct SageMaker integration. SageMaker Pipelines now supports creating and testing pipelines in your local machine (e.g. your computer). With this launch, you can test your Sagemaker Pipelines scripts and parameters compatibility locally before running them in on SageMaker in the cloud. Sagemaker Pipelines Local Mode supports the following steps: processing, training, transform, model, condition, and fail. These steps give you the flexibility to define various entities in your machine learning workflow. Using Pipelines local mode, you can quickly and efficiently debug errors in the scripts and pipeline definition. You can seamlessly switch your workflows from local mode to Sagemaker’s managed environment by updating the session.
This feature is available in all AWS regions where Amazon SageMaker is available. To get started, create a new local mode Sagemaker Pipeline from the Amazon SageMaker Python SDK, and visit our documentation page to learn more.