Posted On: Oct 3, 2019
You can now automate the execution and deployment of end to end machine learning workflows using AWS Step Functions’ enhanced integration with Amazon SageMaker.
AWS Step Functions allows you to build resilient workflows using AWS services such as AWS Glue, AWS Lambda, and Amazon SageMaker. Amazon SageMaker helps developers and data scientists build, train and deploy machine learning (ML) models quickly. Now, with the enhanced integration, Amazon SageMaker users can automate machine learning using serverless workflows. As part of a Step Functions workflow, you can now perform hyperparameter tuning, custom labeling jobs and deploy ML models to the cloud. Step Functions coupled with Amazon SageMaker can both increase the productivity of your data science teams and operate ML pipelines at scale in production.
To get started, review the one-click sample projects that demonstrate how to build train-model-transform and hyper-parameter tuning workflows and then start building your first ML workflow.
For a complete list of regions where AWS Step Functions is offered, see AWS Regions. To learn more:
- Deploy a one-click sample project for the AWS Step Functions integration with Amazon SageMaker
- Read about Managing Amazon SageMaker jobs with Step Functions in the AWS Step Functions Developer Guide.