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

Introduction to CI/CD in AI Workflows

• Specific challenges within AI model delivery pipelines
• Contrasting traditional DevOps with MLOps processes
• Fundamental elements of automated model deployment

Containerising AI Models with Docker

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

Configuring CI/CD Pipelines

• Evaluating CI/CD tooling options and their respective ecosystems
• Structuring pipelines for automated model packaging
• Verifying 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 testing

Automated Deployment of Docker-Based AI Services

• Releasing AI containers to cloud environments
• Executing blue-green and canary release strategies
• Defining rollback mechanisms for failed deployments

Managing Model Versions and Artifacts

• Leveraging registries for model and container version control
• Applying tagging, signing, and image promotion processes
• Synchronising model updates across various services

Monitoring and Observability in AI CI/CD

• Monitoring pipeline efficiency and model performance
• Configuring alerts for build failures or model drift
• Tracing inference behaviour across different environments

Scaling CI/CD Pipelines for AI Systems

• Parallelising builds for large-scale models
• Optimising compute and storage resource usage
• Integrating distributed and remote runners

Summary and Next Steps