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
• Differentiating traditional DevOps and MLOps processes
• Essential components of automated model deployment

Containerizing AI Models with Docker

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

Setting Up CI/CD Pipelines

• Exploring CI/CD tooling options and their respective ecosystems
• Creating pipelines for automated model packaging
• Ensuring pipeline reliability through automated checks

Testing AI Models in CI

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

Automated Deployment of Docker-Based AI Services

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

Managing Model Versions and Artifacts

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

Monitoring and Observability in CI/CD for AI

• 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 usage
• Integrating distributed and remote runners

Summary and Next Steps