Model Ops (MLOps)

Executive Definition: Model Ops (MLOps) — A set of practices that combines machine learning, DevOps, and data engineering to reliably deploy and maintain ML models in production.

MLOps applies DevOps principles to machine learning, automating the training, testing, deployment, and monitoring of models. It ensures that models are reproducible, versioned, and continuously validated. Without MLOps, maintaining model performance over time is difficult, and deployments can become unreliable.

Frequently Asked Questions

What components are in an MLOps pipeline?

A typical pipeline includes data and model versioning, automated training and evaluation, deployment automation, continuous monitoring, and automated retraining triggers.

When does a project need MLOps?

MLOps principles are beneficial even for a single production model to ensure reproducibility and monitoring. They become essential when scaling to multiple models, environments, or collaborating across teams.

What tools are commonly used for MLOps?

Popular tools include MLflow for experiment tracking, Kubeflow for orchestration, DVC for data versioning, Weights & Biases for monitoring, and frameworks like Seldon or BentoML for model serving.