Operationalize forecasting models and Fine-tuned FMs with SageMaker Canvas

Amazon SageMaker Canvas now extends its Model Registry integration to Timeseries forecasting models and SageMaker JumpStart-powered Fine-tuned foundation models. With a single click, you can register these ML models built in Amazon SageMaker Canvas with the SageMaker Model registry, simplifying their deployment to production environments. This enhancement expands the Model registry integration to all problem types supported in Canvas, including regression/classification tabular models, and CV/NLP models. By streamlining the operationalization of ML models in production, Canvas, a no-code tool to build ML models and generate predictions, continues to democratize ML.

With Model registry integration, you can store all the Canvas model artifacts necessary for review, including metadata and performance metrics such as model quality report and explainability report, in a centralized repository to incorporate into your existing CI/CD processes. This allows you to automate your model deployment process by facilitating seamless tracking of model versions, managing approval workflows, and ensuring that only approved models are promoted to production environments.

To access Model registry support for Timeseries forecasting models and SageMaker JumpStart-powered Fine-tuned foundation models access the latest version of SageMaker Canvas. A new user can access the latest version by directly launching SageMaker Canvas from their AWS console. An existing user can access the latest version of SageMaker Canvas by clicking “Log Out” and logging back in.

The expanded feature is now available in all AWS regions where SageMaker Canvas is supported. For more information on leveraging these new capabilities in your ML projects, refer to SageMaker Canvas product documentation.