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

Overview of CI/CD for AI Workflows

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

Encapsulating AI Models with Docker

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

Establishing CI/CD Pipelines

• CI/CD tool choices and their respective 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 verification

Automating the Deployment of Docker-Based AI Services

• Deploying AI containers into cloud environments
• Executing blue-green and canary rollouts
• Rollback tactics for unsuccessful deployments

Administering Model Versions and Artifacts

• Utilizing registries for version control of models and containers
• Tagging, signing, and advancing images
• Synchronizing model updates across services

Monitoring and Observability in AI CI/CD

• Monitoring pipeline and model performance
• Triggering alerts for failed builds or model drift
• Tracing inference behavior across various environments

Scaling CI/CD Pipelines for AI Systems

• Parallelizing builds for extensive models
• Optimizing compute and storage resources
• Incorporating distributed and remote runners

Conclusion and Subsequent Steps