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

Overview of CI/CD in AI Workflows

• Specific challenges associated with AI model delivery pipelines
• Contrasting traditional DevOps and MLOps methodologies
• Essential elements of automated model deployment

Encapsulating AI Models with Docker

• Crafting efficient Dockerfiles for ML inference
• Overseeing dependencies and model assets
• Constructing secure and optimized container images

Configuring CI/CD Pipelines

• Exploring CI/CD tools and their respective ecosystems
• Constructing pipelines for automated model packaging
• Verifying pipelines through automated validation checks

Validating AI Models in CI

• Streamlining data integrity verification
• Conducting unit and integration tests for model services
• Assessing performance and regression validation

Automating the Release of Docker-Based AI Services

• Releasing AI containers into cloud environments
• Executing blue-green and canary release strategies
• Applying rollback procedures for unsuccessful releases

Controlling Model Versions and Artifacts

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

Monitoring and Observability in AI CI/CD

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

Scalability of CI/CD Pipelines for AI Systems

• Parallelizing build processes for large-scale models
• Refining the use of compute and storage resources
• Incorporating distributed and remote runners

Conclusion and Future Steps