AWS launches cross-account SageMaker Pipelines monitoring with CloudWatch dashboards
Track ML pipelines across accounts and regions from a single CloudWatch dashboard.
AWS has released a solution for monitoring Amazon SageMaker Pipelines across multiple AWS accounts and Regions using custom Amazon CloudWatch dashboards. The approach addresses the complexity of distributed ML workloads in MLOps environments. Traditionally, engineers had to manually switch between accounts and regions to inspect pipeline executions. The new solution uses a hub-and-spoke architecture: a primary account hosts the CloudWatch dashboard with DynamoDB tables and Lambda functions for processing, while lightweight Forwarder stacks in each monitored account use EventBridge to send enriched pipeline events to the hub.
The workflow is event-driven and serverless. When a SageMaker Pipeline step changes status, EventBridge rules capture the event, trigger Lambda functions for enrichment, and forward data to the monitoring account. The dashboard then queries DynamoDB via Lambda to display near-real-time execution details—such as ARNs, status, timestamps, and step names—without leaving the AWS Management Console. AWS provides a customizable AWS CDK example on GitHub, helping teams deploy the infrastructure quickly and reduce operational overhead.
- Hub-and-spoke model centralizes monitoring of SageMaker Pipelines across any number of AWS accounts and Regions.
- Serverless, event-driven architecture using EventBridge, Lambda, and DynamoDB eliminates always-on monitoring costs.
- Custom CloudWatch dashboard provides near-real-time visibility into pipeline execution status, steps, and metadata from a single interface.
Why It Matters
Simplifies multi-account MLOps visibility, saving engineering time and preventing pipeline failures from being missed.