Services or capabilities described in Amazon Web Services documentation might vary by Region. To see the differences applicable to the China Regions, see Getting Started with Amazon Web Services in China (PDF).

No-code machine learning with Amazon SageMaker Canvas

Amazon SageMaker Canvas enables you to build your own AI/ML models without having to write a single line of code. You can build ML models for common use cases such as regression and forecasting and can access and evaluate foundation models (FMs) from Amazon Bedrock. You can also access public FMs from Amazon SageMaker JumpStart for content generation, text extraction, and text summarization to support generative AI solutions.

How to build no-code ML models with SageMaker Canvas

Amazon DocumentDB now integrates with Amazon SageMaker Canvas to enable no-code machine learning (ML) with data stored in Amazon DocumentDB. You 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.

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 the SageMaker Canvas workspace from the Amazon Web Services Management 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.

Configuring the SageMaker domain and user profile

You can connect to Amazon DocumentDB clusters from SageMaker domains that are running in VPC Only mode. By launching a SageMaker domain in your VPC, you can control the data flow from your SageMaker Studio and Canvas environments. This allows you to restrict internet access, monitor and inspect traffic using standard Amazon networking and security capabilities, and connect to other Amazon resources through VPC endpoints. Please refer to Amazon SageMaker Canvas Getting started and Configure Amazon SageMaker Canvas in a VPC without internet access located in the Amazon SageMaker Developer Guide to create your SageMaker domain to connect to your Amazon DocumentDB cluster.

Configuring IAM access permissions for Amazon DocumentDB and SageMaker Canvas

An Amazon DocumentDB user that has AmazonDocDBConsoleFullAccess attached to their associated role and identity can access the Amazon Web Services Management Console. Add the following actions to the aforementioned role or identity to provide access to no-code machine learning with Amazon SageMaker Canvas.

"sagemaker:CreatePresignedDomainUrl",
"sagemaker:DescribeDomain",
"sagemaker:ListDomains",
"sagemaker:ListUserProfiles"

Creating database users and roles for SageMaker Canvas

You can restrict access to the actions that users can perform on databases using role-based access control (RBAC) in Amazon DocumentDB. RBAC works by granting one or more roles to a user. These roles determine the operations that a user can perform on database resources.

As a Canvas user, you connect to a Amazon DocumentDB database with username and password credentials. You can create a database user/role for a Canvas user that has read access to the specific databases using Amazon DocumentDBB RBAC functionality.

For example, use the createUser operation:

db.createUser({
user: "canvas_user", 
pwd: "<insert-password>", 
roles: [{role: "read", db: "sample-database-1"}]
})

This creates a canvas_user which has read permissions to the sample-database-1 database. Your Canvas analysts can use this credential to access data in your Amazon DocumentDB cluster. Refer to Database Access Using Role-Based Access Control to learn more.

Available regions

The no-code integration is available in regions where both Amazon DocumentDB and Amazon SageMaker Canvas are supported. The regions include:

Please refer to Amazon SageMaker Canvas in the Amazon SageMaker Developer Guide for the latest region availability.