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

Introduction to CI/CD for AI Workflows

• Distinct challenges associated with AI model delivery pipelines
• Comparing traditional DevOps practices with MLOps processes
• Key components of automated model deployment

Containerizing AI Models with Docker

• Designing efficient Dockerfiles for ML inference
• Managing dependencies and model artifacts effectively
• Constructing secure and optimized container images

Setting Up CI/CD Pipelines

• Exploring CI/CD tooling options and their respective ecosystems
• Building pipelines for automated model packaging
• Validating pipelines using automated checks

Testing AI Models in CI

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

Automated Deployment of Docker-Based AI Services

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

Managing Model Versions and Artifacts

• Utilizing registries for version control of models and containers
• Tagging, signing, and promoting images appropriately
• Coordinating model updates across multiple services

Monitoring and Observability in CI/CD for AI

• Tracking pipeline efficiency and model performance
• Configuring alerts for failed builds 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

Summary and Next Steps