CI/CD for AI: Automating Docker-Based Model Builds and Deployments Training Course

Introduction to CI/CD for AI Workflows

• Distinct challenges in AI model delivery pipelines
• Contrasting traditional DevOps with MLOps processes
• Essential components for automated model deployment

Containerizing AI Models with Docker

• Crafting efficient Dockerfiles for ML inference
• Handling dependencies and model artifacts
• Creating secure and optimized images

Configuring CI/CD Pipelines

• Exploring CI/CD tooling options and their respective ecosystems
• Assembling pipelines for automated model packaging
• Verifying pipelines through automated checks

Testing AI Models within CI

• Automating data integrity validations
• Implementing unit and integration tests for model services
• Conducting performance and regression validations

Automated Deployment of Docker-Based AI Services

• Deploying AI containers into cloud environments
• Executing blue-green and canary release strategies
• Establishing rollback plans for failed deployments

Managing Model Versions and Artifacts

• Leveraging registries for version control of models and containers
• Applying tagging, signing, and promotion workflows to images
• Aligning model updates across multiple services

Monitoring and Observability in AI CI/CD

• Monitoring pipeline and model performance metrics
• Generating alerts for build failures or model drift
• Tracing inference behavior across various environments

Scaling CI/CD Pipelines for AI Systems

• Parallelizing builds to accommodate large models
• Optimizing compute and storage resource utilization
• Incorporating distributed and remote runners

Summary and Future Directions