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

Introduction to CI/CD in AI Workflows

• Specific challenges in AI model delivery pipelines
• Comparison of traditional DevOps and MLOps methodologies
• Essential components of automated model deployment

Containerizing AI Models Using Docker

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

Configuring CI/CD Pipelines

• Evaluating CI/CD tooling options and their ecosystems
• Creating pipelines for automated model packaging
• Validating pipelines via automated checks

Testing AI Models within CI

• Automating data integrity validations
• Conducting unit and integration tests for model services
• Performance and regression verification

Automating Deployment of Docker-Based AI Services

• Deploying AI containers to cloud environments
• Executing blue-green and canary rollout strategies
• Defining rollback procedures for failed deployments

Managing Model Versions and Artifacts

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

Monitoring and Observability in AI CI/CD

• Monitoring pipeline and model performance metrics
• 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 utilization
• Integrating distributed and remote runners

Summary and Future Directions