Building the Final CI/CD and MLOps Pipelines

Introduction

Deploying AI infrastructure is only half the battle. To maintain a healthy engineering culture, you must automate the delivery of both application code and machine learning models. This lesson focuses on unifying CI/CD and MLOps.

Unified Pipelines

A robust pipeline must handle multiple artifacts:

1. Application Containers: The web services and APIs that interface with the models.
2. Model Weights: The serialized artifacts generated from training jobs.
3. Infrastructure Code: The Terraform modules and Kubernetes manifests that define the environment.

GitOps for MLOps

By extending GitOps principles to machine learning, you can achieve reproducibility and auditability. When a new model version is approved, an automated process should update the declarative state in Git, triggering ArgoCD or Flux to deploy the new model to the serving cluster.

Summary

Automated pipelines are the glue that holds a modern platform together. In your capstone project, you will integrate these pipelines into your final architecture.