What is Model Ops (MLOps)?
Model Ops (MLOps) — A set of practices that aims to deploy and maintain machine learning models in production reliably and efficiently.
MLOps applies DevOps principles to machine learning — automating model training, testing, deployment, and monitoring. It ensures models are reproducible, versioned, and continuously validated in production. Without MLOps, models degrade silently and deployments become unreliable.
Frequently Asked Questions
What does an MLOps pipeline include?
Data versioning, automated training, model evaluation, deployment automation, A/B testing, performance monitoring, and automated retraining triggers.
When do I need MLOps?
As soon as you have more than one model in production. Even a single production model benefits from monitoring and versioning. MLOps becomes essential when managing multiple models across teams.
What tools are used for MLOps?
MLflow for experiment tracking, Kubeflow for orchestration, DVC for data versioning, Weights & Biases for monitoring, and Seldon/BentoML for model serving.