GitOps for MLOps: Your ML System as Code

WHY IT EXISTS: Traditional MLOps can involve ad-hoc scripts and manual steps, making it hard to reproduce experiments or audit deployments. GitOps provides a unifying framework to manage this complexity by enforcing a single source of truth.

THE MENTAL MODEL: Treat your entire ML system as a declarative configuration file. A change to data, a hyperparameter, or a deployment target is a pull request. Merging that PR automatically reconciles the live system with the new desired state defined in Git.

HOW IT WORKS: The desired state of the ML system—including data sources, training job specs, and model deployment manifests—is declared in a Git repo. An automated agent, like a Kubernetes operator, constantly monitors the repo and the live cluster. When it detects a difference (e.g., a new commit is pushed), it takes action (e.g., starts a training job, deploys a new model) to make the live state match the Git state.

WHEN TO USE IT: Use it when you need strong reproducibility, auditability, and automation for your ML workflows. It's ideal for teams managing multiple models in production, especially in regulated industries where you must be able to trace a prediction back to the exact code, data, and configuration that produced the model.

WHEN NOT TO USE IT: It can be overkill for early-stage exploration or one-off research projects where speed of iteration is more important than formal process. The initial setup requires significant investment in tooling and defining your ML system declaratively.

ONE CANONICAL EXAMPLE: A data scientist wants to test a new set of hyperparameters. They create a new branch, update a YAML file in the Git repo with the new parameters, and open a pull request. The CI/CD system automatically runs the training job and posts the results back to the PR. Once reviewed and approved, merging the PR could automatically promote the new model to a staging environment.