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

Introduction to CI/CD in AI Workflows

• Distinct challenges within AI model delivery pipelines
• Contrasting traditional DevOps with MLOps processes
• Essential components of automated model deployment

Containerizing AI Models with Docker

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

Establishing CI/CD Pipelines

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

Testing AI Models in CI

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

Automated Deployment of Docker-Based AI Services

• Deploying AI containers to cloud environments
• Executing blue-green and canary rollouts
• Rollback strategies for unsuccessful deployments

Managing Model Versions and Artifacts

• Leveraging registries for model and container versioning
• Tagging, signing, and promoting images
• Synchronizing 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 large models
• Optimizing compute and storage resources
• Integrating distributed and remote runners

Summary and Next Steps