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

Introduction to CI/CD for AI Workflows

• Specific challenges in AI model delivery pipelines
• Differentiating traditional DevOps from MLOps processes
• Essential components of automated model deployment

Containerizing AI Models with Docker

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

Establishing CI/CD Pipelines

• Exploring CI/CD tools and their respective ecosystems
• Configuring pipelines for automated model packaging
• Verifying pipelines through automated checks

Testing AI Models in CI

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

Automated Deployment of Docker-Based AI Services

• Releasing AI containers to cloud environments
• Applying blue-green and canary rollout methods
• Strategies for rolling back failed deployments

Managing Model Versions and Artifacts

• Leveraging registries for model and container versioning
• Tagging, signing, and promoting images
• Synchronizing model updates across 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