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
• Distinguishing between traditional DevOps and MLOps processes
• Key elements of automated model release

Containerization of AI Models using Docker

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

Configuring CI/CD Pipelines

• Evaluation of CI/CD tooling options and their respective ecosystems
• Constructing pipelines for automated model packaging
• Verifying pipelines through automated validation checks

Testing AI Models within CI

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

Automated Release of Docker-Based AI Services

• Releasing AI containers to cloud environments
• Executing blue-green and canary release strategies
• Establishing rollback mechanisms for failed releases

Managing Model Versions and Artifacts

• Leveraging registries for version control of models and containers
• Tagging, signing, and promoting images
• Aligning 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 CI/CD Pipelines for AI Systems

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

Conclusion and Future Directions