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

Foundations of CI/CD for AI Workflows

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

Containerizing AI Models with Docker

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

Configuring CI/CD Pipelines

• Overview of CI/CD tooling options and ecosystems
• Constructing pipelines for automated model packaging
• Verifying pipelines through automated checks

Testing AI Models within CI

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

Automated Deployment of Docker-Based AI Services

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

Managing Model Versions and Artifacts

• Leveraging 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

• Monitoring pipeline and model performance metrics
• Setting alerts for build failures 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

Conclusion and Path Forward