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

Introduction to CI/CD for AI Workflows

• The distinct challenges facing AI model delivery pipelines
• A comparison between traditional DevOps and MLOps processes
• Key components of automated model deployment

Containerizing AI Models with Docker

• Designing efficient Dockerfiles for ML inference
• Handling dependencies and model artifacts
• Constructing secure and optimized images

Configuring CI/CD Pipelines

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

Testing AI Models within CI

• Automating data integrity verifications
• Executing unit and integration tests for model services
• Conducting performance and regression validation

Automating the Deployment of Docker-Based AI Services

• Deploying AI containers to cloud environments
• Implementing blue-green and canary release strategies
• Defining rollback strategies for failed deployments

Managing Model Versions and Artifacts

• Leveraging registries for version control of models and containers
• Tagging, signing, and promoting images
• Coordinating model updates across various services

Monitoring and Observability in AI CI/CD

• Monitoring pipeline and model performance
• Setting up alerts for failed builds or model drift
• Tracing inference behavior across different environments

Scaling CI/CD Pipelines for AI Systems

• Parallelizing builds for large models
• Optimizing compute and storage resources
• Integrating distributed and remote runners

Summary and Future Steps