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

Introduction to CI/CD for AI Workflows

• The distinctive challenges of AI model delivery pipelines
• Contrasting 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

• Evaluating CI/CD tooling options and their ecosystems
• Developing pipelines for automated model packaging
• Verifying pipelines using automated checks

Testing AI Models in CI

• Automating data integrity verification
• Unit and integration testing for model services
• Performance and regression validation

Automated Deployment of Docker-Based AI Services

• Deploying AI containers to cloud environments
• Implementing blue-green and canary release strategies
• Rollback mechanisms for failed deployments

Managing Model Versions and Artifacts

• Utilizing registries for model and container version control
• Tagging, signing, and promoting images
• Coordinating model updates across various 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 different environments

Scaling CI/CD Pipelines for AI Systems

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

Summary and Next Steps