What is MLOps?
Turkish: MLOps
MLOps manages machine learning data, training, deployment, monitoring, and retraining with production engineering discipline.
What is MLOps?
MLOps is the set of practices used to manage machine learning work as reliable production systems instead of isolated experiments. It defines how data science, software engineering, and operations teams collaborate.
What Does It Include?
An MLOps workflow may include data versioning, experiment tracking, a model registry, automated tests, training pipelines, model deployment, performance monitoring, and retraining triggers. Teams monitor not only model accuracy, but also data drift, latency, cost, error rates, and explainability.
Why Is It Needed?
A model that performs well in a notebook may fail in production. Data distributions change, feature calculation code can drift between training and serving, and model outputs may affect business decisions in unexpected ways. MLOps manages those risks through versioning, traceability, and controlled release processes.
DevOps automates software delivery. MLOps extends that discipline to the data and model lifecycle.
Related Terms
DevOps aligns software development and operations through shared processes, automation, metrics, and more reliable delivery.
LLMOpsLLMOps is the practice of testing, monitoring, versioning, and safely operating applications built on large language models.
ML Model DeploymentModel deployment turns a trained machine learning model into an API, batch job, or edge service that runs on production data.