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

Foundations of CI/CD for AI Workflows

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

Encapsulating AI Models with Docker

• Architecting optimized Dockerfiles for ML inference
• Handling dependencies and model assets
• Creating secure and efficient images

Establishing CI/CD Pipelines

• Evaluating CI/CD tooling options and their respective ecosystems
• Developing pipelines for automated model packaging
• Verifying pipelines through automated validation checks

Validating AI Models within CI

• Automating data consistency verification
• Executing unit and integration tests for model services
• Assessing performance and regression validation

Automating the Release of Docker-Based AI Services

• Releasing AI containers to cloud infrastructure
• Implementing blue-green and canary release strategies
• Defining rollback protocols for failed releases

Overseeing Model Versions and Assets

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

Surveillance and Observability in AI CI/CD

• Monitoring pipeline and model performance metrics
• Setting alerts for build failures or model drift
• Tracking inference behavior across different environments

Expanding CI/CD Pipelines for AI Systems

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

Conclusion and Future Directions