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AIOps Modules

The Artificial Intelligence Operations (AIOps) modules provide specialized infrastructure components for building AI/ML workloads and data science platforms on AWS. These modules are designed to accelerate the deployment of machine learning infrastructure, model training, and AI operations.

Repository

Source: AWS AIOps Modules

Available Modules

Coming Soon - Feel free to directly access the Git Repository listed above in the meantime.

We will provide a comprehensive list of:

  • current modules available
  • description of each module
  • input and output definitions
  • example manifest configurations

Key Features

  • ML-Focused: Purpose-built for machine learning and AI workloads
  • Scalable: Designed for enterprise-scale ML operations
  • Integrated: Works seamlessly with AWS AI/ML services
  • Best Practices: Implements MLOps and AIOps best practices
  • Production Ready: Battle-tested components for production ML systems

Usage Example

# Deploy a SageMaker Studio environment
name: ml-studio
path: git::https://github.com/awslabs/aiops-modules.git//modules/sagemaker/sagemaker-studio?ref=main&depth=1
targetAccount: primary
parameters:
  - name: studio-domain-name
    value: my-ml-platform
  - name: enable-projects
    value: true

Common Use Cases

  • ML Platform Setup: Deploy complete SageMaker environments with notebooks, studios, and endpoints
  • Model Training: Set up distributed training with Ray clusters on EKS
  • MLOps Pipelines: Implement CI/CD for machine learning models
  • Experiment Tracking: Deploy MLflow for experiment management and model registry
  • Foundation Models: Deploy and fine-tune foundation models with Bedrock and SageMaker
  • AI Agents: Build intelligent automation and autonomous systems

Getting Started

  1. Identify your ML use case from the categories above
  2. Review module documentation for specific AI/ML components
  3. Configure parameters for your ML workload requirements
  4. Deploy using Seed-Farmer for consistent, repeatable ML infrastructure

For more information about building ML platforms with Seed-Farmer, see the Module Development Guide.