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

Introduction to CI/CD for AI Workflows

• Specific challenges associated with AI model delivery pipelines
• Contrasting traditional DevOps with MLOps processes
• Key components involved in automated model deployment

Containerizing AI Models with Docker

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

Configuring CI/CD Pipelines

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

Testing AI Models within CI

• Automating data integrity validations
• Conducting unit and integration tests for model services
• Validating performance and detecting regressions

Automating Deployment of Docker-Based AI Services

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

Managing Model Versions and Artifacts

• Leveraging registries for version control of models and containers
• Applying tagging, signing, and image promotion practices
• Synchronizing model updates across various services

Monitoring and Observability in AI CI/CD

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

Scaling CI/CD Pipelines for AI Systems

• Parallelizing builds to accommodate large models
• Optimizing the use of compute and storage resources
• Integrating distributed and remote runner capabilities

Summary and Recommended Next Steps