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

Foundations of CI/CD in AI Workflows

• Specific challenges in delivering AI model pipelines
• Distinguishing between conventional DevOps and MLOps methodologies
• Essential elements of automated model deployment

Encapsulating AI Models with Docker

• Crafting efficient Dockerfiles for machine learning inference
• Handling dependencies and model artifacts effectively
• Constructing secure and high-performance images

Establishing CI/CD Pipelines

• Examining CI/CD tooling choices and their respective ecosystems
• Assembling pipelines for the automated packaging of models
• Ensuring pipeline reliability through automated validation checks

Validating AI Models within CI

• Automating checks for data consistency
• Executing unit and integration tests for model services
• Verifying performance and preventing regression issues

Automating the Deployment of Docker-Based AI Services

• Releasing AI containers to cloud infrastructures
• Executing blue-green and canary release strategies
• Defining rollback procedures for unsuccessful deployments

Controlling Model Versions and Artifacts

• Leveraging registries for managing model and container versions
• Applying tagging, signing, and image promotion protocols
• Synchronizing model updates across multiple services

Integrating Monitoring and Observability in AI CI/CD

• Monitoring the performance of both pipelines and models
• Generating alerts for build failures or model drift
• Tracing inference behavior across various environments

Expanding CI/CD Pipelines for Large-Scale AI Systems

• Parallelizing build processes for large models
• Enhancing the efficiency of compute and storage resources
• Incorporating distributed and remote execution runners

Conclusions and Future Directions