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

Introduction to CI/CD in AI Workflows

• Distinct challenges associated with AI model delivery pipelines
• Comparison of traditional DevOps and MLOps processes
• Essential components for automated model deployment

Containerizing AI Models with Docker

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

Configuring CI/CD Pipelines

• Exploring CI/CD tooling options and their respective ecosystems
• Constructing pipelines for automated model packaging
• Validating 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 Deployment of Docker-Based AI Services

• Deploying AI containers into cloud environments
• Executing blue-green and canary rollout strategies
• Implementing rollback procedures for unsuccessful deployments

Managing Model Versions and Artifacts

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

Monitoring and Observability in AI CI/CD

• Tracking the performance of pipelines and models
• Setting alerts for build failures or model drift
• Tracing inference behavior across different environments

Scaling CI/CD Pipelines for AI Systems

• Parallelizing builds for large-scale models
• Optimizing the use of compute and storage resources
• Integrating distributed and remote runners

Conclusion and Future Steps