Amazon SageMaker Canvas can now operationalize ML models in production

You can now register machine learning (ML) models built in Amazon SageMaker Canvas with a single click to SageMaker Model registry enabling you to operationalize ML models in production. SageMaker Canvas is a visual interface that enables business analysts to generate accurate ML predictions on their own — without requiring any ML experience or having to write a single line of code.

With SageMaker Canvas you can automatically create ML models to run what if analysis and generate single or bulk predictions. Now with SageMaker Model Registry integration, you can store all model artifacts including metadata and performance metrics baselines to a central repository and plug them into your existing model deployment CI/CD processes. A model registry plays a key role in the model deployment process because it packages all model information and enables the automation of model promotion to production environments. Starting today, you can select a model version in SageMaker Canvas, register it to SageMaker Model Registry in your own account and track the approval status of the same. Rejecting a model in registry prevents the model from being deployed into an escalated environment, whereas approving a model in the registry can trigger a model promotion pipeline that automatically copies the model to the pre-production AWS account, and get your model ready for production inferencing workloads.

The ability to register Amazon SageMaker Canvas ML models to SageMaker Model Registry is now available in all AWS regions where SageMaker Canvas is supported. To learn more, refer to the AWS News Blog and SageMaker Canvas product documentation.