Amazon DocumentDB (with MongoDB compatibility) now integrates with Amazon SageMaker Canvas to enable no-code Machine learning (ML) with data stored in Amazon DocumentDB. Customers can now build ML models for regression and forecasting needs and use foundation models for content summarization and generation using data stored in Amazon DocumentDB without writing a single line of code. The new integration removes the undifferentiated heavy lifting when customers connect and access data in Amazon DocumentDB and accelerates ML development with a no-code experience.

SageMaker Canvas provides a visual interface that allows Amazon DocumentDB customers to generate predictions without requiring any AI/ML expertise or write a single line of code. Customers can now launch SageMaker Canvas workspace from the Amazon DocumentDB Console, import and join Amazon DocumentDB data for data preparation and model training. Data in Amazon DocumentDB can now be used in SageMaker Canvas to build and augment models to predict customer churn, detect fraud, predict maintenance failures, forecast business metrics, and generate content. Customers can now publish and share ML-driven insights across teams using SageMaker Canvas’s native integration with Amazon QuickSight. Data ingestion pipelines in SageMaker Canvas run on Amazon DocumentDB secondary instances by default, ensuring that the performance of application and SageMaker Canvas ingestion workloads are not hindered.

Amazon DocumentDB customers can get started with SageMaker Canvas by navigating to the new Amazon DocumentDB No-Code ML Console page and connecting to new or available SageMaker Canvas workspaces. The integration is generally available in regions where both Amazon DocumentDB and SageMaker Canvas are available.