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

Introduction to CI/CD for AI Workflows

• Distinct challenges in AI model delivery pipelines
• Comparison of traditional DevOps and MLOps processes
• Key components of automated model deployment

Containerizing AI Models with Docker

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

Configuring CI/CD Pipelines

• Exploring CI/CD tooling options and their ecosystems
• Establishing pipelines for automated model packaging
• Verifying pipelines through automated checks

Testing AI Models within CI

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

Automating the Deployment of Docker-Based AI Services

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

Managing Model Versions and Artifacts

• Utilizing registries for model and container version control
• Applying tagging, signing, and image promotion practices
• Coordinating model updates across multiple services

Monitoring and Observability in AI CI/CD

• Tracking pipeline and model performance metrics
• 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 resource usage
• Integrating distributed and remote runners

Summary and Next Steps