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Both services **entered maintenance mode on 2026-07-30** — AWS no longer onboards new customers and will not add new features. **Existing deployments continue to work**, so this is a non-breaking notice, not a removal.\n>\n> **Migration path:** The MDAA team is **targeting a replacement solution in the next release**. It is expected to be based on the AWS-recommended open-source [SageMaker AI monitoring stack](https://github.com/aws-samples/sample-aiops-on-amazon-sagemakerai/tree/main/monitoring) (Evidently AI + EventBridge + Lambda), Amazon CloudWatch metrics/anomaly detection, Amazon QuickSight dashboards, and [SHAP](https://shap.readthedocs.io/en/latest/)-based explainability. **Adopt that replacement when it ships** rather than migrating ad hoc. For background, see the [Model Monitor availability change](https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor-availability-change.html) and the [Clarify availability change](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-availability-change.html).\n>\n> This deprecation is delivered in phases (deprecate → replace → remove).\n\nDeploys SageMaker Model Monitor schedules for all four monitoring types — data quality, model quality, model bias, and model explainability — against a deployed real-time endpoint. Each monitor runs as a scheduled processing job that compares live inference traffic against baseline statistics and constraints, publishing violations to S3 and CloudWatch. Use this module when you need continuous monitoring of a production inference endpoint to detect data drift, model degradation, bias drift, or changes in feature attribution.\n\n---\n\n## Deployed Resources\n\nThis module deploys and integrates the following resources:\n\n**SageMaker Data Quality Monitoring Schedule** (Optional) - Scheduled processing job that compares incoming request data against baseline data statistics and constraints.\n\n**SageMaker Model Quality Monitoring Schedule** (Optional) - Scheduled processing job that evaluates model prediction accuracy against ground truth labels.\n\n**SageMaker Model Bias Monitoring Schedule** (Optional) - Scheduled processing job that detects bias drift in model predictions using SageMaker Clarify.\n\n**SageMaker Model Explainability Monitoring Schedule** (Optional) - Scheduled processing job that tracks changes in feature attribution using SageMaker Clarify.\n\n**Baseline Processing Job** (Optional) - One-time processing job that generates baseline statistics and constraints from a representative dataset.\n\n**Amazon S3 Output Bucket** - Stores monitoring output reports, constraint violations, and baseline artifacts.\n\n**AWS KMS Key** - Customer-managed encryption key for S3 output bucket and processing job storage volumes.\n\n**AWS IAM Monitoring Role** - Execution role for monitoring processing jobs with permissions to read endpoint data capture and write results to S3.\n\n<!-- Architecture diagram not yet available -->\n\n---\n\n## Related Modules\n\n- [SageMaker Endpoint](../sagemaker-endpoint-app/README.md) — Deploys the real-time inference endpoint that this module monitors for drift and quality degradation\n- [SageMaker MLOps](../sagemaker-mlops-app/README.md) — Provides the model artifacts and model package group used to establish monitoring baselines\n- [SageMaker Studio Domain](../sm-studio-domain-app/README.md) — Provides SageMaker domain tagging context for resource governance\n\n---\n\n## Security/Compliance Details\n\nThis module is designed in alignment with MDAA security/compliance principles and CDK nag rulesets. Additional review is recommended prior to production deployment, ensuring organization-specific compliance requirements are met.\n\n- **Encryption at Rest**:\n  - S3 output bucket encrypted with customer-managed KMS key\n  - Processing job storage volumes encrypted with KMS\n  - Baseline artifacts encrypted at rest\n- **Encryption in Transit**:\n  - All S3 access enforced over HTTPS via bucket policy\n  - Processing job containers communicate over TLS\n- **Least Privilege**:\n  - Monitoring role scoped to specific endpoint, S3 paths, and KMS key\n  - KMS key policy restricts usage to the monitoring role and admin principals\n- **Network Isolation**:\n  - Monitoring processing jobs support VPC configuration with security groups and subnets\n  - Optional network isolation mode prevents containers from making outbound network calls\n\n---\n\n## Configuration\n\n### MDAA Config\n\nAdd the following snippet to your mdaa.yaml under the `modules:` section of a domain/env in order to use this module:\n\n```yaml\nsagemaker-model-monitoring: # Module Name can be customized\n  module_path: '@aws-mdaa/sagemaker-model-monitoring' # Must match module NPM package name\n  module_configs:\n    - ./sagemaker-model-monitoring.yaml # Filename/path can be customized\n```\n\n### Module Config Samples and Variants\n\nCopy the contents of the relevant sample config below into the `./sagemaker-model-monitoring.yaml` file referenced in the MDAA config snippet above.\n\n#### Minimal Configuration\n\nStart here for a single data quality monitor on an existing endpoint with default schedule and instance settings.\n\n[sample-config-minimal.yaml](sample_configs/sample-config-minimal.yaml)\n\n```yaml\n--8<-- \"target/docs/packages/apps/ai/sagemaker-model-monitoring-app/sample_configs/sample-config-minimal.yaml\"\n```\n\n#### Comprehensive Configuration\n\nUse this as a reference when you need all four monitor types, custom baseline generation, VPC isolation, and per-monitor schedule and instance configuration.\n\n[sample-config-comprehensive.yaml](sample_configs/sample-config-comprehensive.yaml)\n\n```yaml\n--8<-- \"target/docs/packages/apps/ai/sagemaker-model-monitoring-app/sample_configs/sample-config-comprehensive.yaml\"\n```\n","readmeFilename":"README.md"}