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

Foundations of CI/CD for AI Workflows

• Distinct challenges encountered in AI model delivery pipelines.
• A comparison between traditional DevOps and MLOps methodologies.
• Essential components for automated model deployment.

Containerization of AI Models Using Docker

• Constructing efficient Dockerfiles tailored for ML inference.
• Handling dependencies and model artifacts.
• Creating secure and performance-optimized images.

Configuring CI/CD Pipelines

• Exploring CI/CD tooling alternatives and their respective ecosystems.
• Assembling pipelines for automated model packaging.
• Ensuring pipeline reliability through automated checks.

Testing AI Models Within CI

• Automating data integrity verification.
• Executing unit and integration tests for model services.
• Conducting performance and regression validation.

Automated Release of Docker-Enabled AI Services

• Deploying AI containers into cloud environments.
• Applying blue-green and canary release techniques.
• Defining rollback procedures for unsuccessful deployments.

Overseeing Model Versions and Artifacts

• Leveraging registries for version control of models and containers.
• Implementing tagging, signing, and image promotion.
• Synchronizing model updates across different services.

Surveillance and Visibility in AI CI/CD

• Monitoring pipeline efficiency and model performance.
• Generating alerts for build failures or model drift.
• Tracing inference behavior across various environments.

Expanding CI/CD Pipelines for AI Systems

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

Conclusions and Future Directions