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

Introduction to CI/CD for AI Workflows

• Specific challenges associated with AI model delivery pipelines.
• A comparison of traditional DevOps versus MLOps processes.
• Key components of automated model deployment.

Containerizing AI Models with Docker

• Crafting efficient Dockerfiles for ML inference.
• Managing dependencies and model artifacts.
• Constructing secure and optimized images.

Setting Up CI/CD Pipelines

• Reviewing CI/CD tooling options and their respective ecosystems.
• Developing pipelines for automated model packaging.
• Validating pipelines through automated checks.

Testing AI Models in CI

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

Automated Deployment of Docker-Based AI Services

• Deploying AI containers to cloud environments.
• Implementing blue-green and canary rollout strategies.
• Defining rollback strategies for failed 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 CI/CD for AI

• 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 resource usage.
• Integrating distributed and remote runners.

Summary and Next Steps