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

Introduction to AI-Centric CI/CD Workflows

• Distinct challenges in delivering AI model pipelines
• Contrasting traditional DevOps with MLOps methodologies
• Essential elements of automated model deployment

Containerization of AI Models Using Docker

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

Configuration of CI/CD Pipelines

• Exploring CI/CD tooling options and their respective ecosystems
• Designing pipelines for automated model packaging
• Ensuring pipeline reliability through automated checks

Validating AI Models Within CI

• Automating data integrity verification
• Executing unit and integration tests for model services
• Conducting performance and regression assessments

Automated Release of Docker-Based AI Services

• Releasing AI containers to cloud infrastructures
• Applying blue-green and canary release patterns
• Defining rollback procedures for unsuccessful deployments

Handling Model Versions and Artifacts

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

Monitoring and Observability in AI CI/CD

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

Scaling AI System CI/CD Pipelines

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

Wrap-up and Future Directions