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

Introduction to CI/CD in AI Environments

• Distinct challenges associated with AI model delivery pipelines
• Differentiating traditional DevOps from MLOps processes
• Essential elements of automated model deployment

Containerizing AI Models via Docker

• Architecting efficient Dockerfiles for ML inference tasks
• Handling dependencies and model artifacts effectively
• Creating secure and performance-optimized images

Establishing CI/CD Pipelines

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

Testing AI Models Within CI

• Automating checks for data integrity
• Executing unit and integration tests for model services
• Conducting performance and regression validation

Automated Deployment of Docker-Hosted AI Services

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

Overseeing Model Versions and Artifacts

• Leveraging registries for version control of models and containers
• Applying tagging, signing, and image promotion practices
• Synchronizing model updates across various services

Monitoring and Observability in AI CI/CD

• Monitoring pipeline execution and model performance metrics
• Generating alerts for build failures or model drift
• Tracking inference behavior across different environments

Scaling AI CI/CD Pipelines

• Parallelizing builds to accommodate large models
• Optimizing usage of compute and storage resources
• Integrating distributed and remote runner capabilities

Conclusion and Future Directions