Add a module for Data Quality Monitoring Job for a SageMaker Endpoint.

CHANGELOG.md

@@ -11,6 +11,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### **Added**
 
+- Added a `sagemaker-model-monitoring-module` module with an example of data quality monitoring of a SageMaker Endpoint.
 - Added an option to enable data capture in the `sagemaker-endpoint-module`.
 
 ### **Changed**

README.md

@@ -32,6 +32,7 @@ See deployment steps in the [Deployment Guide](DEPLOYMENT.md).
 | [SageMaker Custom Kernel Module](modules/sagemaker/sagemaker-custom-kernel/README.md)                                     | Builds custom kernel for SageMaker Studio from a Dockerfile                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |
 | [SageMaker Model Package Group Module](modules/sagemaker/sagemaker-model-package-group/README.md)                         | Creates a SageMaker Model Package Group to register and version SageMaker Machine Learning (ML) models and setups an Amazon EventBridge Rule to send model package group state change events to an Amazon EventBridge Bus                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |
 | [SageMaker Model Package Promote Pipeline Module](modules/sagemaker/sagemaker-model-package-promote-pipeline/README.md)   | Deploy a Pipeline to promote SageMaker Model Packages in a multi-account setup. The pipeline can be triggered through an EventBridge rule in reaction of a SageMaker Model Package Group state event change (Approved/Rejected). Once the pipeline is triggered, it will promote the latest approved model package, if one is found.                                                                                                                                                                                                                                                                                                                                                                                                                                    |
+| [SageMaker Model Monitoring Module](modules/sagemaker/sagemaker-model-monitoring-module/README.md)                        | Deploy a data quality monitoring job which runs against a SageMaker Endpoint.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |
 
 ### Mlflow Modules
 

modules/sagemaker/sagemaker-model-monitoring/README.md

@@ -0,0 +1,95 @@
+# SageMaker Model Monitoring
+
+## Description
+
+This module creates a SageMaker Model Monitoring job for data quality.
+It requires a deployed model endpoint and the proper check steps
+for each monitoring job:
+
+* Data Quality: [QualityCheck step](https://docs.aws.amazon.com/sagemaker/latest/dg/build-and-manage-steps.html#step-type-quality-check)
+
+### Architecture
+
+![SageMaker Model Monitoring Module Architecture](docs/_static/sagemaker-model-monitoring-module-architecture.png "SageMaker Model Monitoring Module Architecture")
+
+## Inputs/Outputs
+
+### Input Parameters
+
+#### Required
+
+- `endpoint-name`: The name of the endpoint used to run the monitoring job.
+- `security-group-id`: The VPC security group IDs, should provide access to the given `subnet-ids`.
+- `subnet-ids`: The ID of the subnets in the VPC to which you want to connect your training job or model.
+- `model-package-arn`: Model package ARN
+- `model-bucket-arn`: S3 bucket ARN for model artifacts
+- `kms-key-id`: The KMS key used to encrypted storage and output.
+- `data-quality-checkstep-output-prefix`: The S3 prefix in `model-artifacts-bucket-arn` which contains the output from the corresponding [Check step in the SageMaker Pipeline](https://docs.aws.amazon.com/sagemaker/latest/dg/build-and-manage-steps.html#build-and-manage-steps-types).
+- `data-quality-output-prefix`: The S3 prefix in `model-artifacts-bucket-arn` to contain the output of the monitoring job.
+
+
+#### Optional
+
+- `sagemaker-project-id`: SageMaker project id
+- `sagemaker-project-name`: SageMaker project name
+- `data-quality-instance-count`: The number of ML compute instances to use in the model monitoring job.
+- `data-quality-instance-type`: The ML compute instance type for the processing job.
+- `data-quality-instance-volume-size-in-gb`: The size of the ML storage volume, in gigabytes, that you want to provision.
+- `data-quality-max-runtime-in-seconds`: The maximum length of time, in seconds, the monitoring job can run before it is stopped.
+- `data-quality-schedule-expression`: A cron expression that describes details about the monitoring schedule.
+
+### Sample manifest declaration
+
+```yaml
+name: monitoring
+path: modules/sagemaker/sagemaker-model-monitoring
+parameters:
+  - name: sagemaker_project_id
+    value: dummy123
+  - name: sagemaker_project_name
+    value: dummy123
+  - name: model_package_arn
+    value: arn:aws:sagemaker:<region>:<account>:model-package/<package_name>/1
+  - name: model_bucket_arn
+    value: arn:aws:s3:::<bucket name>
+  - name: data-quality-checkstep-output-prefix
+    value: model-training-run-1234/dataqualitycheckstep
+  - name: data-quality-output-prefix
+    value: model-training-run-1234/monitor/dataqualityoutput
+  - name: endpoint_name
+    valueFrom:
+      moduleMetadata:
+        group: endpoints
+        name: endpoint
+        key: EndpointName
+  - name: security_group_id
+    valueFrom:
+      moduleMetadata:
+        group: endpoints
+        name: endpoint
+        key: SecurityGroupId
+  - name: kms_key_id
+    valueFrom:
+      moduleMetadata:
+        group: endpoints
+        name: endpoint
+        key: KmsKeyId
+  - name: subnet_ids
+    valueFrom:
+      moduleMetadata:
+        group: networking
+        name: networking
+        key: PrivateSubnetIds
+```
+
+### Module Metadata Outputs
+
+- `ModelExecutionRoleArn`: SageMaker Model Execution IAM role ARN
+- `ModelName`: SageMaker Model name
+- `ModelPackageArn`: SageMaker Model package ARN
+- `EndpointName`: SageMaker Endpoint name
+- `EndpointUrl`: SageMaker Endpoint Url
+
+#### Output Example
+
+N/A

modules/sagemaker/sagemaker-model-monitoring/app.py

@@ -0,0 +1,30 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+import os
+
+import aws_cdk
+import cdk_nag
+
+from sagemaker_model_monitoring.settings import ApplicationSettings
+from sagemaker_model_monitoring.stack import SageMakerModelMonitoringStack
+
+# Load application settings from env vars.
+app_settings = ApplicationSettings()
+
+environment = aws_cdk.Environment(
+    account=os.environ["CDK_DEFAULT_ACCOUNT"],
+    region=os.environ["CDK_DEFAULT_REGION"],
+)
+
+app = aws_cdk.App()
+stack = SageMakerModelMonitoringStack(
+    scope=app,
+    id=app_settings.settings.app_prefix,
+    env=environment,
+    **app_settings.parameters.model_dump(),
+)
+
+aws_cdk.Aspects.of(app).add(cdk_nag.AwsSolutionsChecks(log_ignores=False))
+
+app.synth()

modules/sagemaker/sagemaker-model-monitoring/deployspec.yaml

@@ -0,0 +1,25 @@
+publishGenericEnvVariables: true
+deploy:
+  phases:
+    install:
+      commands:
+      - env
+      # Install whatever additional build libraries
+      - npm install -g [email protected]
+      - pip install -r requirements.txt
+    build:
+      commands:
+      - cdk deploy --require-approval never --progress events --app "python app.py" --outputs-file ./cdk-exports.json
+      # Export metadata
+      - seedfarmer metadata convert -f cdk-exports.json || true
+destroy:
+  phases:
+    install:
+      commands:
+      # Install whatever additional build libraries
+      - npm install -g [email protected]
+      - pip install -r requirements.txt
+    build:
+      commands:
+      # execute the CDK
+      - cdk destroy --force --app "python app.py"

modules/sagemaker/sagemaker-model-monitoring/docs/_static/sagemaker-model-monitoring-module-architecture.xml

@@ -0,0 +1 @@
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</diagram></mxfile>

modules/sagemaker/sagemaker-model-monitoring/pyproject.toml

@@ -0,0 +1,41 @@
+[tool.ruff]
+exclude = [
+    ".eggs",
+    ".git",
+    ".hg",
+    ".mypy_cache",
+    ".ruff_cache",
+    ".tox",
+    ".venv",
+    "_build",
+    "buck-out",
+    "build",
+    "dist",
+    "codeseeder",
+]
+line-length = 120
+target-version = "py38"
+
+[tool.ruff.lint]
+select = ["F", "I", "E", "W"]
+fixable = ["ALL"]
+
+[tool.mypy]
+python_version = "3.8"
+strict = true
+ignore_missing_imports = true
+disallow_untyped_decorators = false
+exclude = "codeseeder.out/|example/|tests/"
+warn_unused_ignores = false
+
+[tool.pytest.ini_options]
+addopts = "-v --cov=. --cov-report term"
+pythonpath = [
+  "."
+]
+
+[tool.coverage.run]
+omit = ["tests/*"]
+
+[tool.coverage.report]
+fail_under = 80

modules/sagemaker/sagemaker-model-monitoring/requirements-dev.in

@@ -0,0 +1,12 @@
+awscli
+cdk-nag
+cfn-lint
+check-manifest
+mypy
+pip-tools
+pydot
+pyroma
+pytest
+ruff
+types-PyYAML
+types-setuptools