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

Introduction to CI/CD for AI Workflows

• Specific challenges in AI model delivery pipelines
• Comparing traditional DevOps with MLOps processes
• Fundamental components of automated model deployment

Containerizing AI Models with Docker

• Crafting 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 ecosystems
• Creating pipelines for automated model packaging
• Verifying pipelines through automated checks

Testing AI Models within CI

• Automating data integrity verification
• Unit and integration testing for model services
• Performance and regression validation

Automating Deployment of Docker-Based AI Services

• Deploying AI containers to cloud environments
• Executing blue-green and canary rollouts
• Rollback procedures for failed deployments

Managing Model Versions and Artifacts

• Leveraging registries for model and container version control
• Tagging, signing, and promoting images
• Coordinating model updates across multiple services

Monitoring and Observability in AI CI/CD

• Monitoring pipeline and model performance
• Alerting on failed builds or model drift
• Tracing inference behavior across various environments

Scaling CI/CD Pipelines for AI Systems

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

Summary and Next Steps