SMUS CI/CD Pipeline CLI

[Preview] Amazon SageMaker Unified Studio CI/CD CLI is currently in preview and is subject to change. Commands, configuration formats, and APIs may evolve based on customer feedback. We recommend evaluating this tool in non-production environments during preview. For feedback and bug reports, please open an issue https://github.com/aws/CICD-for-SageMakerUnifiedStudio/issues

[IAM Domains Only] This CLI currently supports SMUS domains using IAM-based authentication only. Support for IAM Identity Center (IdC)-based domains is coming soon.

Automate deployment of data applications across SageMaker Unified Studio environments

Deploy Airflow DAGs, Jupyter notebooks, and ML workflows from development to production with confidence. Built for data scientists, data engineers, ML engineers, and GenAI app developers working with DevOps teams.

Works with your deployment strategy: Whether you use git branches (branch-based), versioned artifacts (bundle-based), git tags (tag-based), or direct deployment - this CLI supports your workflow. Define your application once, deploy it your way.

Why SMUS CI/CD CLI?

✅ AWS Abstraction Layer - CLI encapsulates all AWS analytics, ML, and SMUS complexity - DevOps teams never call AWS APIs directly
✅ Separation of Concerns - Data teams define WHAT to deploy (manifest.yaml), DevOps teams define HOW and WHEN (CI/CD workflows)
✅ Generic CI/CD Workflows - Same workflow works for Glue, SageMaker, Bedrock, QuickSight, or any AWS service combination
✅ Deploy with Confidence - Automated testing and validation before production
✅ Multi-Environment Management - Test → Prod with environment-specific configuration
✅ Infrastructure as Code - Version-controlled application manifests and reproducible deployments
✅ Event-Driven Workflows - Trigger workflows automatically via EventBridge on deployment

Quick Start

Install from source:

git clone https://github.com/aws/CICD-for-SageMakerUnifiedStudio.git
cd CICD-for-SageMakerUnifiedStudio
pip install -e .

Deploy your first application:

# Validate configuration
smus-cli describe --manifest manifest.yaml --connect

# Create deployment bundle (optional)
smus-cli bundle --manifest manifest.yaml

# Deploy to test environment
smus-cli deploy --targets test --manifest manifest.yaml

# Run validation tests
smus-cli test --manifest manifest.yaml --targets test

See it in action: Live GitHub Actions Example

Who Is This For?

👨‍💻 Data Teams (Data Scientists, Data Engineers, GenAI App Developers)

You focus on: Your application - what to deploy, where to deploy, and how it runs
You define: Application manifest (manifest.yaml) with your code, workflows, and configurations
You don't need to know: CI/CD pipelines, GitHub Actions, deployment automation

→ Quick Start Guide - Deploy your first application in 10 minutes

Includes examples for:

Data Engineering (Glue, Notebooks, Athena)
ML Workflows (SageMaker, Notebooks)
GenAI Applications (Bedrock, Notebooks)

🔧 DevOps Teams

You focus on: CI/CD best practices, security, compliance, and deployment automation
You define: Workflow templates that enforce testing, approvals, and promotion policies
You don't need to know: Application-specific details, AWS services used, DataZone APIs, SMUS project structures, or business logic

→ Admin Guide - Configure infrastructure and pipelines in 15 minutes
→ GitHub Workflow Templates - Generic, reusable workflow templates for automated deployment

The CLI is your abstraction layer: You just call smus-cli deploy - the CLI handles all AWS service interactions (DataZone, Glue, Athena, SageMaker, MWAA, S3, IAM, etc.). Your workflows stay simple and generic.

What Can You Deploy?

📊 Analytics & BI

Glue ETL jobs and crawlers
Athena queries
QuickSight dashboards
EMR jobs (future)
Redshift queries (future)

🤖 Machine Learning

SageMaker training jobs
ML models and endpoints
MLflow experiments
Feature Store (future)
Batch transforms (future)

🧠 Generative AI

Bedrock agents
Knowledge bases
Foundation model configurations (future)

📓 Code & Workflows

Jupyter notebooks
Python scripts
Airflow DAGs (MWAA and Amazon MWAA Serverless)
Lambda functions (future)

💾 Data & Storage

S3 data files
Git repositories
Data catalogs (future)

Supported AWS Services

Deploy workflows using these AWS services through Airflow YAML syntax:

🎯 Analytics & Data

Amazon Athena • AWS Glue • Amazon EMR • Amazon Redshift • Amazon QuickSight • Lake Formation

🤖 Machine Learning

SageMaker Training • SageMaker Pipelines • Feature Store • Model Registry • Batch Transform

🧠 Generative AI

Amazon Bedrock • Bedrock Agents • Bedrock Knowledge Bases • Guardrails

📊 Additional Services

S3 • Lambda • Step Functions • DynamoDB • RDS • SNS/SQS • Batch

See complete list: Airflow AWS Operators Reference

Core Concepts

Separation of Concerns: The Key Design Principle

The Problem: Traditional deployment approaches force DevOps teams to learn AWS analytics services (Glue, Athena, DataZone, SageMaker, MWAA, etc.) and understand SMUS project structures, or force data teams to become CI/CD experts.

The Solution: SMUS CLI is the abstraction layer that encapsulates all AWS and SMUS complexity.

Example workflow:

1. DevOps Team                 2. Data Team                    3. SMUS CLI (The Abstraction)
   ↓                               ↓                              ↓
Defines the PROCESS            Defines the CONTENT            Workflow calls:
- Test on merge                - Glue jobs                    smus-cli deploy --manifest manifest.yaml
- Approval for prod            - SageMaker training             ↓
- Security scans               - Athena queries               CLI handles ALL AWS complexity:
- Notification rules           - File structure               - DataZone APIs
                                                              - Glue/Athena/SageMaker APIs
Defines INFRASTRUCTURE                                        - MWAA deployment
- Account & region                                            - S3 management
- IAM roles                                                   - IAM configuration
- Resources                                                   - Infrastructure provisioning

Works for ANY app!
No ML/Analytics/GenAI
service knowledge needed!

DevOps teams focus on:

CI/CD best practices (testing, approvals, notifications)
Security and compliance gates
Deployment orchestration
Monitoring and alerting

SMUS CLI handles ALL AWS complexity:

DataZone domain and project management
AWS Glue, Athena, SageMaker, MWAA APIs
S3 storage and artifact management
IAM roles and permissions
Connection configurations
Catalog asset subscriptions
Workflow deployment to Airflow
Infrastructure provisioning
Testing and validation

Data teams focus on:

Application code and workflows
Which AWS services to use (Glue, Athena, SageMaker, etc.)
Environment configurations
Business logic

Result:

DevOps teams never call AWS APIs directly - they just call smus-cli deploy
CI/CD workflows are generic - same workflow works for Glue apps, SageMaker apps, or Bedrock apps
Data teams never touch CI/CD configs
Both teams work independently using their expertise

Application Manifest

A declarative YAML file (manifest.yaml) that defines your data application:

Application details - Name, version, description
Content - Code from git repositories, data/models from storage, QuickSight dashboards
Workflows - Airflow DAGs for orchestration and automation
Stages - Where to deploy (dev, test, prod environments)
Configuration - Environment-specific settings, connections, and bootstrap actions

Created and owned by data teams. Defines what to deploy and where. No CI/CD knowledge required.

Application

Your data/analytics workload being deployed:

Airflow DAGs and Python scripts
Jupyter notebooks and data files
ML models and training code
ETL pipelines and transformations
GenAI agents and MCP servers
Foundation model configurations

Stage

A deployment environment (dev, test, prod) mapped to a SageMaker Unified Studio project:

Domain and region configuration
Project name and settings
Resource connections (S3, Airflow, Athena, Glue)
Environment-specific parameters
Optional branch mapping for git-based deployments

Stage-to-Project Mapping

Each application stage deploys to a dedicated SageMaker Unified Studio (SMUS) project. A project can host a single application or multiple applications depending on your architecture and CI/CD methodology. Stage projects are independent entities with their own governance:

Ownership & Access: Each stage project has its own set of owners and contributors, which may differ from the development project. Production projects typically have restricted access compared to development environments.
Multi-Domain & Multi-Region: Stage projects can belong to different SMUS domains, AWS accounts, and regions. For example, your dev stage might deploy to a development domain in us-east-1, while prod deploys to a production domain in eu-west-1.
Flexible Architecture: Organizations can choose between dedicated projects per application (isolation) or shared projects hosting multiple applications (consolidation), based on security, compliance, and operational requirements.

This separation enables true environment isolation with independent access controls, compliance boundaries, and regional data residency requirements.

Workflow

Orchestration logic that executes your application. Workflows serve two purposes:

1. Deployment-time: Create required AWS resources during deployment

Provision infrastructure (S3 buckets, databases, IAM roles)
Configure connections and permissions
Set up monitoring and logging

2. Runtime: Execute ongoing data and ML pipelines

Scheduled execution (daily, hourly, etc.)
Event-driven triggers (S3 uploads, API calls)
Data processing and transformations
Model training and inference

Workflows are defined as Airflow DAGs (Directed Acyclic Graphs) in YAML format. Supports MWAA (Managed Workflows for Apache Airflow) and Amazon MWAA Serverless (User Guide).

CI/CD Automation

GitHub Actions workflows (or other CI/CD systems) that automate deployment:

Created and owned by DevOps teams
Defines how and when to deploy
Runs tests and quality gates
Manages promotion across targets
Enforces security and compliance policies
Example: .github/workflows/deploy.yml

Key insight: DevOps teams create generic, reusable workflows that work for ANY application. They don't need to know if the app uses Glue, SageMaker, or Bedrock - the CLI handles all AWS service interactions. The workflow just calls smus-cli deploy and the CLI does the rest.

Deployment Modes

Bundle-based (Artifact): Create versioned archive → deploy archive to stages

Good for: audit trails, rollback capability, compliance
Command: smus-cli bundle then smus-cli deploy --manifest app.tar.gz

Direct (Git-based): Deploy directly from sources without intermediate artifacts

Good for: simpler workflows, rapid iteration, git as source of truth
Command: smus-cli deploy --manifest manifest.yaml --stage test

Both modes work with any combination of storage and git content sources.

Example Applications

Real-world examples showing how to deploy different workloads with SMUS CI/CD.

📊 Analytics - QuickSight Dashboard

Deploy interactive BI dashboards with automated Glue ETL pipelines for data preparation. Uses QuickSight asset bundles, Athena queries, and GitHub dataset integration with environment-specific configurations.

AWS Services: QuickSight • Glue • Athena • S3 • MWAA Serverless

GitHub Workflow: analytic-dashboard-glue-quicksight.yml

What happens during deployment: Application code is deployed to S3, Glue jobs and Airflow workflows are created and executed, QuickSight dashboard/data source/dataset are created, and QuickSight ingestion is initiated to refresh the dashboard with latest data.

📁 App Structure

dashboard-glue-quick/
├── manifest.yaml                      # Deployment configuration
├── covid_etl_workflow.yaml           # Airflow workflow definition
├── glue_setup_covid_db.py            # Glue job: Create database & tables
├── glue_covid_summary_job.py         # Glue job: ETL transformations
├── glue_set_permission_check.py      # Glue job: Permission validation
├── quicksight/
│   └── TotalDeathByCountry.qs        # QuickSight dashboard bundle
└── app_tests/
    └── test_covid_data.py            # Integration tests

Key Files:

Glue Jobs: Python scripts for database setup, ETL, and validation
Workflow: YAML defining Airflow DAG for orchestration
QuickSight Bundle: Dashboard, datasets, and data sources
Tests: Validate data quality and dashboard functionality

View Airflow Workflow

workflow_combined:
  dag_id: 'covid_dashboard_glue_quick_pipeline'
  tasks:
    setup_covid_db_task:
      operator: airflow.providers.amazon.aws.operators.glue.GlueJobOperator
      retries: 0
      job_name: setup-covid-db-job
      script_location: '{proj.connection.default.s3_shared.s3Uri}dashboard-glue-quick/bundle/glue_setup_covid_db.py'
      s3_bucket: '{proj.connection.default.s3_shared.bucket}'
      iam_role_name: '{proj.iam_role_name}'
      region_name: '{domain.region}'
      update_config: true
      script_args:
        '--BUCKET_NAME': '{proj.connection.default.s3_shared.bucket}'
        '--REGION_NAME': '{domain.region}'
      create_job_kwargs:
        GlueVersion: '4.0'
        MaxRetries: 0
        Timeout: 180

    data_summary_task:
      operator: airflow.providers.amazon.aws.operators.glue.GlueJobOperator
      retries: 0
      job_name: summary-glue-job
      script_location: '{proj.connection.default.s3_shared.s3Uri}dashboard-glue-quick/bundle/glue_covid_summary_job.py'
      s3_bucket: '{proj.connection.default.s3_shared.bucket}'
      iam_role_name: '{proj.iam_role_name}'
      region_name: '{domain.region}'
      update_config: true
      script_args:
        '--DATABASE_NAME': 'covid19_db'
        '--TABLE_NAME': 'us_simplified'
        '--SUMMARY_DATABASE_NAME': 'covid19_summary_db'
        '--S3_DATABASE_PATH': '{proj.connection.default.s3_shared.s3Uri}dashboard-glue-quick/output/databases/covid19_summary_db/'
        '--BUCKET_NAME': '{proj.connection.default.s3_shared.bucket}'
      dependencies: [setup_covid_db_task]
      create_job_kwargs:
        GlueVersion: '4.0'
        MaxRetries: 0
        Timeout: 180

    set_permission_check_task:
      operator: airflow.providers.amazon.aws.operators.glue.GlueJobOperator
      retries: 0
      job_name: set-permission-check-job
      script_location: '{proj.connection.default.s3_shared.s3Uri}dashboard-glue-quick/bundle/glue_set_permission_check.py'
      s3_bucket: '{proj.connection.default.s3_shared.bucket}'
      iam_role_name: '{proj.iam_role_name}'
      region_name: '{domain.region}'
      update_config: true
      script_args:
        '--BUCKET_NAME': '{proj.connection.default.s3_shared.bucket}'
        '--REGION_NAME': '{domain.region}'
        '--ROLES': '{env.GRANT_TO}'
      dependencies: [data_summary_task]
      create_job_kwargs:
        GlueVersion: '4.0'
        MaxRetries: 0
        Timeout: 180

View Manifest

applicationName: IntegrationTestETLWorkflow

content:
  storage:
  - name: dashboard-glue-quick
    include:
    - "*.py"
  - name: workflows
    include:
    - "*.yaml"
  
  git:
  - repository: covid-19-dataset
    url: https://github.com/datasets/covid-19.git
  
  quicksight:
  - name: TotalDeathByCountry
    type: dashboard
  
  workflows:
  - workflowName: covid_dashboard_glue_quick_pipeline
    connectionName: default.workflow_serverless

stages:
  test:
    stage: TEST
    domain:
      tags:
        purpose: smus-cicd-testing
      region: ${TEST_DOMAIN_REGION:us-east-1}
    project:
      name: test-marketing
      owners:
      - Eng1
      - arn:aws:iam::${AWS_ACCOUNT_ID}:role/GitHubActionsRole-SMUS-CLI-Tests
      - arn:aws:iam::${AWS_ACCOUNT_ID}:role/Admin
    environment_variables:
      S3_PREFIX: test
      AWS_REGION: ${TEST_DOMAIN_REGION:us-east-1}
      GRANT_TO: Admin,service-role/aws-quicksight-service-role-v0
    bootstrap:
      actions:
      - type: workflow.create
        workflowName: covid_dashboard_glue_quick_pipeline
      - type: workflow.run
        workflowName: covid_dashboard_glue_quick_pipeline
        trailLogs: true
      - type: quicksight.refresh_dataset
        refreshScope: IMPORTED
        ingestionType: FULL_REFRESH
        wait: false
    deployment_configuration:
      storage:
      - name: dashboard-glue-quick
        connectionName: default.s3_shared
        targetDirectory: dashboard-glue-quick/bundle
      - name: workflows
        connectionName: default.s3_shared
        targetDirectory: dashboard-glue-quick/bundle/workflows
      git:
      - name: covid-19-dataset
        connectionName: default.s3_shared
        targetDirectory: repos
      quicksight:
        assets:
        - name: TotalDeathByCountry
          owners:
          - arn:aws:quicksight:${TEST_DOMAIN_REGION:us-east-1}:${AWS_ACCOUNT_ID}:user/default/Admin/*
          viewers:
          - arn:aws:quicksight:${TEST_DOMAIN_REGION:us-east-1}:${AWS_ACCOUNT_ID}:user/default/Admin/*
        overrideParameters:
          ResourceIdOverrideConfiguration:
            PrefixForAllResources: deployed-{stage.name}-covid-

View Full Example →

📓 Data Engineering - Notebooks

Deploy Jupyter notebooks with parallel execution orchestration for data analysis and ETL workflows. Demonstrates notebook deployment with MLflow integration for experiment tracking.

AWS Services: SageMaker Notebooks • MLflow • S3 • MWAA Serverless

GitHub Workflow: analytic-data-notebooks.yml

What happens during deployment: Notebooks and workflow definitions are uploaded to S3, Airflow DAG is created for parallel notebook execution, MLflow connection is provisioned for experiment tracking, and notebooks are ready to run on-demand or scheduled.

📁 App Structure

data-notebooks/
├── manifest.yaml                                # Deployment configuration
├── notebooks/
│   ├── customer_churn_prediction.ipynb         # Customer churn ML
│   ├── retail_sales_forecasting.ipynb          # Sales forecasting
│   ├── customer_segmentation_analysis.ipynb    # Customer segmentation
│   └── requirements.txt                        # Python dependencies
├── workflows/
│   └── parallel_notebooks_workflow.yaml        # Airflow orchestration
└── app_tests/
    └── test_notebooks_execution.py             # Integration tests

Key Files:

Notebooks: 3 Jupyter notebooks for ML and analytics workflows
Workflow: Parallel execution orchestration with Airflow
Tests: Validate notebook execution and outputs

View Manifest

applicationName: IntegrationTestNotebooks

content:
  storage:
    - name: notebooks
      connectionName: default.s3_shared
      include:
        - notebooks/
        - workflows/
  
  workflows:
    - workflowName: parallel_notebooks_execution
      connectionName: default.workflow_serverless

stages:
  test:
    domain:
      region: us-east-1
    project:
      name: test-marketing
      owners:
        - Eng1
        - arn:aws:iam::${AWS_ACCOUNT_ID}:role/GitHubActionsRole-SMUS-CLI-Tests
    environment_variables:
      S3_PREFIX: test
    deployment_configuration:
      storage:
        - name: notebooks
          connectionName: default.s3_shared
          targetDirectory: notebooks/bundle/notebooks
    bootstrap:
      actions:
        - type: datazone.create_connection
          name: mlflow-server
          connection_type: MLFLOW
          properties:
            trackingServerArn: arn:aws:sagemaker:${STS_REGION}:${STS_ACCOUNT_ID}:mlflow-tracking-server/smus-integration-mlflow-use2
            trackingServerName: smus-integration-mlflow-use2
        - type: workflow.create
          workflowName: parallel_notebooks_execution
        - type: workflow.run
          workflowName: parallel_notebooks_execution
          trailLogs: true

View Airflow Workflow

notebooks_workflow:
  dag_id: notebooks_parallel
  tasks:
    nb_churn:
      operator: airflow.providers.amazon.aws.operators.sagemaker_unified_studio.SageMakerNotebookOperator
      retries: 0
      input_config:
        input_path: notebooks/bundle/notebooks/customer_churn_prediction.ipynb
        input_params: {}
      output_config:
        output_formats:
        - NOTEBOOK
      compute:
        instance_type: ml.c5.xlarge
        image_details:
          image_name: sagemaker-distribution-prod
          image_version: '3'
      wait_for_completion: true
    nb_sales:
      operator: airflow.providers.amazon.aws.operators.sagemaker_unified_studio.SageMakerNotebookOperator
      retries: 0
      input_config:
        input_path: notebooks/bundle/notebooks/retail_sales_forecasting.ipynb
        input_params: {}
      output_config:
        output_formats:
        - NOTEBOOK
      compute:
        instance_type: ml.c5.xlarge
        image_details:
          image_name: sagemaker-distribution-prod
          image_version: '3'
      wait_for_completion: true
    nb_segment:
      operator: airflow.providers.amazon.aws.operators.sagemaker_unified_studio.SageMakerNotebookOperator
      retries: 0
      input_config:
        input_path: notebooks/bundle/notebooks/customer_segmentation_analysis.ipynb
        input_params: {}
      output_config:
        output_formats:
        - NOTEBOOK
      compute:
        instance_type: ml.c5.xlarge
        image_details:
          image_name: sagemaker-distribution-prod
          image_version: '3'
      wait_for_completion: true

View Full Example →

🤖 Machine Learning - Training

Train ML models with SageMaker using the SageMaker SDK and SageMaker Distribution images. Track experiments with MLflow and automate training pipelines with environment-specific configurations.

AWS Services: SageMaker Training • MLflow • S3 • MWAA Serverless

GitHub Workflow: analytic-ml-training.yml

What happens during deployment: Training code and workflow definitions are uploaded to S3 with compression, Airflow DAG is created for training orchestration, MLflow connection is provisioned for experiment tracking, and SageMaker training jobs are created and executed using SageMaker Distribution images.

📁 App Structure

ml/training/
├── manifest.yaml                      # Deployment configuration
├── code/
│   ├── sagemaker_training_script.py  # Training script
│   └── requirements.txt              # Python dependencies
├── workflows/
│   ├── ml_training_workflow.yaml     # Airflow orchestration
│   └── ml_training_notebook.ipynb    # Training notebook
└── app_tests/
    └── test_model_registration.py    # Integration tests

Key Files:

Training Script: SageMaker training job implementation
Workflow: Airflow DAG for training orchestration
Notebook: Interactive training workflow
Tests: Validate model registration and training

View Manifest

applicationName: IntegrationTestMLTraining

content:
  storage:
    - name: training-code
      connectionName: default.s3_shared
      include: [ml/training/code]
    
    - name: training-workflows
      connectionName: default.s3_shared
      include: [ml/training/workflows]
  
  workflows:
    - workflowName: ml_training_workflow
      connectionName: default.workflow_serverless

stages:
  test:
    domain:
      region: us-east-1
    project:
      name: test-ml-training
      owners:
        - Eng1
        - arn:aws:iam::${AWS_ACCOUNT_ID}:role/GitHubActionsRole-SMUS-CLI-Tests
      role:
        arn: arn:aws:iam::${AWS_ACCOUNT_ID}:role/SMUSCICDTestRole
    environment_variables:
      S3_PREFIX: test
    deployment_configuration:
      storage:
        - name: training-code
          connectionName: default.s3_shared
          targetDirectory: ml/bundle/training-code
          compression: gz
        - name: training-workflows
          connectionName: default.s3_shared
          targetDirectory: ml/bundle/training-workflows
    bootstrap:
      actions:
        - type: datazone.create_connection
          name: mlflow-server
          connection_type: MLFLOW
          properties:
            trackingServerArn: arn:aws:sagemaker:${STS_REGION}:${STS_ACCOUNT_ID}:mlflow-tracking-server/smus-integration-mlflow-use2
        - type: workflow.create
          workflowName: ml_training_workflow
        - type: workflow.run
          workflowName: ml_training_workflow
          trailLogs: true

View Airflow Workflow

ml_training_workflow:
  dag_id: "ml_training_workflow"
  tasks:
    ml_training_notebook:
      operator: "airflow.providers.amazon.aws.operators.sagemaker_unified_studio.SageMakerNotebookOperator"
      retries: 0
      input_config:
        input_path: "ml/bundle/training-workflows/ml_training_notebook.ipynb"
        input_params:
          mlflow_tracking_server_arn: "{proj.connection.mlflow-server.trackingServerArn}"
          mlflow_artifact_location: "{proj.connection.default.s3_shared.s3Uri}ml/mlflow-artifacts"
          sklearn_version: "1.2-1"
          python_version: "py3"
          training_instance_type: "ml.m5.large"
          model_name: "realistic-classifier-v1"
      output_config:
        output_formats: 
          ['NOTEBOOK']
      wait_for_completion: True

View Full Example →

🤖 Machine Learning - Deployment

Deploy trained ML models as SageMaker real-time inference endpoints. Uses SageMaker SDK for endpoint configuration and SageMaker Distribution images for serving.

AWS Services: SageMaker Endpoints • S3 • MWAA Serverless

GitHub Workflow: analytic-ml-deployment.yml

What happens during deployment: Model artifacts, deployment code, and workflow definitions are uploaded to S3, Airflow DAG is created for endpoint deployment orchestration, SageMaker endpoint configuration and model are created, and the inference endpoint is deployed and ready to serve predictions.

📁 App Structure

ml/deployment/
├── manifest.yaml                      # Deployment configuration
├── code/
│   └── inference.py                  # Inference handler
├── workflows/
│   ├── ml_deployment_workflow.yaml   # Airflow orchestration
│   └── ml_deployment_notebook.ipynb  # Deployment notebook
└── app_tests/
    └── test_endpoint_deployment.py   # Integration tests

Key Files:

Inference Handler: Custom inference logic for endpoint
Workflow: Airflow DAG for endpoint deployment
Notebook: Interactive deployment workflow
Tests: Validate endpoint deployment and predictions

View Manifest

applicationName: IntegrationTestMLDeployment

content:
  storage:
    - name: deployment-code
      connectionName: default.s3_shared
      include: [ml/deployment/code]
    
    - name: deployment-workflows
      connectionName: default.s3_shared
      include: [ml/deployment/workflows]
    
    - name: model-artifacts
      connectionName: default.s3_shared
      include: [ml/output/model-artifacts/latest]
  
  workflows:
    - workflowName: ml_deployment_workflow
      connectionName: default.workflow_serverless

stages:
  test:
    domain:
      region: us-east-1
    project:
      name: test-ml-deployment
      owners:
        - Eng1
        - arn:aws:iam::${AWS_ACCOUNT_ID}:role/GitHubActionsRole-SMUS-CLI-Tests
      role:
        arn: arn:aws:iam::${AWS_ACCOUNT_ID}:role/SMUSCICDTestRole
    environment_variables:
      S3_PREFIX: test
    deployment_configuration:
      storage:
        - name: deployment-code
          connectionName: default.s3_shared
          targetDirectory: ml/bundle/deployment-code
        - name: deployment-workflows
          connectionName: default.s3_shared
          targetDirectory: ml/bundle/deployment-workflows
        - name: model-artifacts
          connectionName: default.s3_shared
          targetDirectory: ml/bundle/model-artifacts
    bootstrap:
      actions:
        - type: workflow.create
          workflowName: ml_deployment_workflow
        - type: workflow.run
          workflowName: ml_deployment_workflow
          trailLogs: true

View Airflow Workflow

ml_deployment_workflow:
  dag_id: "ml_deployment_workflow"
  tasks:
    ml_deployment_notebook:
      operator: "airflow.providers.amazon.aws.operators.sagemaker_unified_studio.SageMakerNotebookOperator"
      retries: 0
      input_config:
        input_path: "ml/bundle/deployment-workflows/ml_deployment_notebook.ipynb"
        input_params:
          model_s3_uri: "{proj.connection.default.s3_shared.s3Uri}ml/output/model-artifacts/latest/output/model.tar.gz"
          sklearn_version: "1.2-1"
          python_version: "py3"
          inference_instance_type: "ml.m5.large"
      output_config:
        output_formats: 
          ['NOTEBOOK']
      wait_for_completion: True

View Full Example →

🧠 Generative AI

Deploy GenAI applications with Bedrock agents and knowledge bases. Demonstrates RAG (Retrieval Augmented Generation) workflows with automated agent deployment and testing.

AWS Services: Amazon Bedrock • S3 • MWAA Serverless

GitHub Workflow: analytic-genai-workflow.yml

What happens during deployment: Agent configuration and workflow definitions are uploaded to S3, Airflow DAG is created for agent deployment orchestration, Bedrock agents and knowledge bases are configured, and the GenAI application is ready for inference and testing.

📁 App Structure

genai/
├── manifest.yaml                      # Deployment configuration
├── job-code/
│   ├── requirements.txt              # Python dependencies
│   ├── test_agent.yaml               # Agent test configuration
│   ├── lambda_mask_string.py         # Lambda function
│   └── utils/
│       ├── bedrock_agent.py          # Agent management
│       ├── bedrock_agent_helper.py   # Agent utilities
│       └── knowledge_base_helper.py  # Knowledge base utilities
├── workflows/
│   ├── genai_dev_workflow.yaml       # Airflow orchestration
│   └── bedrock_agent_notebook.ipynb  # Agent deployment notebook
└── app_tests/
    └── test_genai_workflow.py        # Integration tests

Key Files:

Agent Code: Bedrock agent and knowledge base management
Workflow: Airflow DAG for GenAI deployment
Notebook: Interactive agent deployment
Tests: Validate agent functionality

View Manifest

applicationName: IntegrationTestGenAIWorkflow

content:
  storage:
    - name: agent-code
      connectionName: default.s3_shared
      include: [genai/job-code]
    
    - name: genai-workflows
      connectionName: default.s3_shared
      include: [genai/workflows]
  
  workflows:
    - workflowName: genai_dev_workflow
      connectionName: default.workflow_serverless

stages:
  test:
    domain:
      region: us-east-1
    project:
      name: test-marketing
      owners:
        - Eng1
        - arn:aws:iam::${AWS_ACCOUNT_ID}:role/GitHubActionsRole-SMUS-CLI-Tests
    environment_variables:
      S3_PREFIX: test
    deployment_configuration:
      storage:
        - name: agent-code
          connectionName: default.s3_shared
          targetDirectory: genai/bundle/agent-code
        - name: genai-workflows
          connectionName: default.s3_shared
          targetDirectory: genai/bundle/workflows

View Airflow Workflow

genai_dev_workflow:
  dag_id: "genai_dev_workflow"
  tasks:
    bedrock_agent_notebook:
      operator: "airflow.providers.amazon.aws.operators.sagemaker_unified_studio.SageMakerNotebookOperator"
      retries: 0
      input_config:
        input_path: "genai/bundle/workflows/bedrock_agent_notebook.ipynb"
        input_params:
          agent_name: "calculator_agent"
          agent_llm: "us.anthropic.claude-3-5-sonnet-20241022-v2:0"
          force_recreate: "True"
          kb_name: "mortgage-kb"
      output_config:
        output_formats: 
          ['NOTEBOOK']
      wait_for_completion: True

View Full Example →

See All Examples with Detailed Walkthroughs →

📋 Feature Checklist

Legend: ✅ Supported | 🔄 Planned | 🔮 Future

Core Infrastructure

Feature	Status	Notes
YAML configuration	✅	Manifest Guide
Infrastructure as Code	✅	Deploy Command
Multi-environment deployment	✅	Stages
CLI tool	✅	CLI Commands
Version control integration	✅	GitHub Actions

Deployment & Bundling

Automated Deployment - Define your application content, workflows, and deployment targets in YAML. Bundle-based (artifact) or direct (git-based) deployment modes. Deploy to test and prod with a single command. Dynamic configuration using ${VAR} substitution. Track deployments in S3 or git for deployment history.

Feature	Status	Notes
Artifact bundling	✅	Bundle Command
Bundle-based deployment	✅	Deploy Command
Direct deployment	✅	Deploy Command
Deployment validation	✅	Describe Command
Incremental deployment	🔄	Upload only changed files
Rollback support	🔮	Automated rollback
Blue-green deployment	🔮	Zero-downtime deployments

Developer Experience

Feature	Status	Notes
Project templates	🔄	`smus-cli init` with templates
Manifest initialization	✅	Create Command
Interactive setup	🔄	Guided configuration prompts
Local development	✅	CLI Commands
VS Code extension	🔮	IntelliSense and validation

Configuration

Environment Variables & Dynamic Configuration - Flexible configuration for any environment using variable substitution. Environment-specific settings with validation and connection management.

Feature	Status	Notes
Variable substitution	✅	Substitutions Guide
Environment-specific config	✅	Stages
Secrets management	🔮	AWS Secrets Manager integration
Config validation	✅	Manifest Schema
Connection management	✅	Connections Guide

Resources & Workloads

Deploy Any AWS Service - Airflow DAGs, Jupyter notebooks, Glue ETL jobs, Athena queries, SageMaker training and endpoints, QuickSight dashboards, Bedrock agents, Lambda functions, EMR jobs, and Redshift queries.

Feature	Status	Notes
Airflow DAGs	✅	Workflows
Jupyter notebooks	✅	SageMakerNotebookOperator
Glue ETL jobs	✅	GlueJobOperator
Athena queries	✅	AthenaOperator
SageMaker training	✅	SageMakerTrainingOperator
SageMaker endpoints	✅	SageMakerEndpointOperator
QuickSight dashboards	✅	QuickSight Deployment
Bedrock agents	✅	BedrockInvokeModelOperator
Lambda functions	🔄	LambdaInvokeFunctionOperator
EMR jobs	✅	EmrAddStepsOperator
Redshift queries	✅	RedshiftDataOperator

Bootstrap Actions

Automated Workflow Execution & Event-Driven Workflows - Trigger workflows automatically during deployment with workflow.run (use trailLogs: true to stream logs and wait for completion). Fetch workflow logs for validation and debugging with workflow.logs. Automatically refresh QuickSight dashboards after ETL deployment with quicksight.refresh_dataset. Emit custom events for downstream automation and CI/CD orchestration with eventbridge.put_events. Provision MLflow and other DataZone connections during deployment. Actions run in order during smus-cli deploy for reliable initialization and validation.

Feature	Status	Notes
Workflow execution	✅	workflow.run
Log retrieval	✅	workflow.logs
QuickSight refresh	✅	quicksight.refresh_dataset
EventBridge events	✅	eventbridge.put_events
DataZone connections	✅	datazone.create_connection
Sequential execution	✅	Execution Flow

CI/CD Integration

Pre-built CI/CD Pipeline Workflows - GitHub Actions, GitLab CI, Azure DevOps, and Jenkins support for automated deployment. Flexible configuration for any CI/CD platform. Trigger deployments from external events with webhook support.

Feature	Status	Notes
GitHub Actions	✅	GitHub Actions Guide
GitLab CI	✅	CLI Commands
Azure DevOps	✅	CLI Commands
Jenkins	✅	CLI Commands
Service principals	✅	GitHub Actions Guide
OIDC federation	✅	GitHub Actions Guide

Testing & Validation

Automated Tests & Quality Gates - Run validation tests before promoting to production. Block deployments if tests fail. Track execution status and logs. Verify deployment correctness with health checks.

Feature	Status	Notes
Unit testing	✅	Test Command
Integration testing	✅	Test Command
Automated tests	✅	Test Command
Quality gates	✅	Test Command
Workflow monitoring	✅	Monitor Command

Monitoring & Observability

Feature	Status	Notes
Deployment monitoring	✅	Deploy Command
Workflow monitoring	✅	Monitor Command
Custom alerts	✅	Deployment Metrics
Metrics collection	✅	Deployment Metrics
Deployment history	✅	Bundle Command

AWS Service Integration

Feature	Status	Notes
Amazon MWAA	✅	Workflows
MWAA Serverless	✅	Workflows
AWS Glue	✅	Airflow Operators
Amazon Athena	✅	Airflow Operators
SageMaker	✅	Airflow Operators
Amazon Bedrock	✅	Airflow Operators
Amazon QuickSight	✅	QuickSight Deployment
DataZone	✅	Manifest Schema
EventBridge	✅	Deployment Metrics
Lake Formation	✅	Connections Guide
Amazon S3	✅	Storage
AWS Lambda	🔄	Airflow Operators
Amazon EMR	✅	Airflow Operators
Amazon Redshift	✅	Airflow Operators

Advanced Features

Feature	Status	Notes
Multi-region deployment	✅	Stages
Cross-project deployment	✅	Stages
Dependency management	✅	Airflow Operators
Catalog subscriptions	✅	Manifest Schema
Multi-service orchestration	✅	Airflow Operators
Drift detection	🔮	Detect configuration drift
State management	🔄	Comprehensive state tracking

Documentation

Getting Started

Quick Start Guide - Deploy your first application (10 min)
Admin Guide - Set up infrastructure (15 min)

Guides

Application Manifest - Complete YAML configuration reference
CLI Commands - All available commands and options
Bootstrap Actions - Automated deployment actions and event-driven workflows
Substitutions & Variables - Dynamic configuration
Connections Guide - Configure AWS service integrations
GitHub Actions Integration - CI/CD automation setup
Deployment Metrics - Monitoring with EventBridge

Reference

Manifest Schema - YAML schema validation and structure
Airflow AWS Operators - Custom operator reference

Examples

Examples Guide - Walkthrough of example applications
Data Notebooks - Jupyter notebooks with Airflow
ML Training - SageMaker training with MLflow
ML Deployment - SageMaker endpoint deployment
QuickSight Dashboard - BI dashboards with Glue
GenAI Application - Bedrock agents and knowledge bases

Development

Developer Guide - Complete development guide with architecture, testing, and workflows
AI Assistant Context - Context for AI assistants (Amazon Q, Kiro)
Tests Overview - Testing infrastructure

Support

Issues: GitHub Issues
Documentation: docs/
Examples: examples/

Security Notice

⚠️ DO NOT install from PyPI - always install from official AWS source code.

# ✅ Correct - Install from official AWS repository
git clone https://github.com/aws/CICD-for-SageMakerUnifiedStudio.git
cd CICD-for-SageMakerUnifiedStudio
pip install -e .

# ❌ Wrong - Do not use PyPI
pip install smus-cicd-cli  # May contain malicious code

License

This project is licensed under the MIT-0 License. See LICENSE for details.

Scan QR code to view this README on GitHub

Name		Name	Last commit message	Last commit date
Latest commit History 864 Commits
.github/workflows		.github/workflows
.kiro		.kiro
developer		developer
docs		docs
examples		examples
git-templates		git-templates
src/smus_cicd		src/smus_cicd
tests		tests
.coveragerc		.coveragerc
.gitignore		.gitignore
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
MANIFEST.in		MANIFEST.in
NOTICE		NOTICE
README.md		README.md
pyproject.toml		pyproject.toml
pytest.ini		pytest.ini
requirements-dev.txt		requirements-dev.txt
requirements.txt		requirements.txt
setup.cfg		setup.cfg
setup.py		setup.py
test-coverage.xml		test-coverage.xml

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SMUS CI/CD Pipeline CLI

Why SMUS CI/CD CLI?

Quick Start

Who Is This For?

👨‍💻 Data Teams (Data Scientists, Data Engineers, GenAI App Developers)

🔧 DevOps Teams

What Can You Deploy?

Supported AWS Services

🎯 Analytics & Data

🤖 Machine Learning

🧠 Generative AI

📊 Additional Services

Core Concepts

Separation of Concerns: The Key Design Principle

Application Manifest

Application

Stage

Stage-to-Project Mapping

Workflow

CI/CD Automation

Deployment Modes

Example Applications

📊 Analytics - QuickSight Dashboard

📓 Data Engineering - Notebooks

🤖 Machine Learning - Training

🤖 Machine Learning - Deployment

🧠 Generative AI

📋 Feature Checklist

Core Infrastructure

Deployment & Bundling

Developer Experience

Configuration

Resources & Workloads

Bootstrap Actions

CI/CD Integration

Testing & Validation

Monitoring & Observability

AWS Service Integration

Advanced Features

Documentation

Getting Started

Guides

Reference

Examples

Development

Support

Security Notice

License

About

Resources

License

Code of conduct

Contributing

Security policy

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