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

Foundations of CI/CD for AI Workflows

• Distinct challenges inherent to AI model delivery pipelines
• Analysis of traditional DevOps versus MLOps processes
• Essential elements of automated model deployment

Encapsulating AI Models with Docker

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

Configuring CI/CD Pipelines

• Overview of CI/CD tooling and their respective ecosystems
• Constructing pipelines for automated model packaging
• Verifying pipeline integrity through automated checks

Validating AI Models within CI

• Automating data integrity verification
• Implementing unit and integration tests for model services
• Conducting performance and regression validation

Automating the Release of Docker-Based AI Services

• Deploying AI containers to cloud infrastructures
• Executing blue-green and canary release strategies
• Defining rollback procedures for unsuccessful deployments

Governing Model Versions and Artifacts

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

Monitoring and Observability in AI CI/CD

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

Scaling CI/CD Pipelines for AI Systems

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

Conclusion and Future Directions