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

Foundations of CI/CD for AI Workflows

• The specific challenges associated with AI model delivery pipelines.
• A comparison between traditional DevOps practices and MLOps processes.
• Essential components involved in automated model deployment.

Containerizing AI Models Using Docker

• Designing efficient Dockerfiles optimized for ML inference.
• Handling dependencies and managing model artifacts effectively.
• Constructing secure and high-performance container images.

Configuring CI/CD Pipelines

• Evaluating CI/CD tooling options and their respective ecosystems.
• Developing pipelines for the automated packaging of models.
• Ensuring pipeline reliability through automated validation checks.

Testing AI Models within CI

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

Automating the Deployment of Docker-Based AI Services

• Deploying AI containers into cloud-based environments.
• Implementing blue-green and canary release strategies.
• Establishing effective rollback protocols for failed deployments.

Overseeing Model Versions and Artifacts

• Leveraging registries for the version control of models and containers.
• Applying tagging, signing, and promotion workflows for images.
• Ensuring seamless coordination of model updates across various services.

Monitoring and Observability in AI CI/CD

• Tracking the performance of both pipelines and models.
• Setting up alerts for build failures or model drift issues.
• Tracing inference behavior consistently across different environments.

Scaling CI/CD Pipelines for AI Systems

• Parallelizing build processes for large-scale models.
• Optimizing the utilization of compute and storage resources.
• Integrating distributed and remote runner capabilities.

Conclusions and Future Directions