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

Introduction to CI/CD in AI Workflows

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

Containerizing AI Models Using Docker

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

Establishing CI/CD Pipelines

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

Testing AI Models in CI

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

Automated Deployment of Docker-Based AI Services

• Releasing AI containers into cloud environments
• Executing blue-green and canary release strategies
• Rollback procedures for unsuccessful deployments

Overseeing Model Versions and Artifacts

• Leveraging registries for model and container version management
• Tagging, signing, and promoting images
• Aligning model updates across services

Monitoring and Observability in AI CI/CD

• Monitoring pipeline and model performance
• Setting 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 computational and storage resources
• Integrating distributed and remote runners

Conclusion and Future Steps