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

Introduction to CI/CD in AI Workflows

• Distinct challenges facing AI model delivery pipelines
• Contrasting traditional DevOps with MLOps methodologies
• Key elements of automated model deployment

Containerization of AI Models via Docker

• Crafting efficient Dockerfiles for ML inference tasks
• Handling dependencies and managing model artifacts
• Constructing secure and high-performance images

Establishing CI/CD Pipelines

• Evaluating CI/CD toolsets and their respective ecosystems
• Creating pipelines for the automated packaging of models
• Verifying pipelines through automated quality checks

Testing AI Models within CI

• Automating data integrity validations
• Executing unit and integration tests for model services
• Conducting performance and regression assessments

Automating the Release of Docker-Based AI Services

• Releasing AI containers to cloud infrastructure
• Applying blue-green and canary release strategies
• Defining rollback plans for unsuccessful deployments

Versioning and Artifact Management

• Leveraging registries for model and container version tracking
• Tagging, signing, and promoting images
• Synchronizing model updates across multiple services

Monitoring and Observability for AI CI/CD

• Maintaining oversight of pipeline and model performance
• Setting alerts for build failures or model drift
• Tracing inference behavior across different environments

Scaling CI/CD Pipelines for AI Systems

• Parallelizing build processes for large-scale models
• Optimizing compute and storage resource utilization
• Incorporating distributed and remote runners

Recap and Future Directions