MLOps, short for Machine Learning Operations, is a set of practices and technologies used to develop, deploy, monitor, and maintain machine learning models throughout their lifecycle. It combines principles from machine learning, software development, and IT operations to create a structured process for moving models from experimentation into reliable production environments.
For example, a company developing a demand-forecasting model can use MLOps to manage training data, automate model training and testing, deploy updated models, and continuously monitor their performance. When data patterns change or model accuracy declines, the system can trigger retraining or other corrective actions. MLOps helps organizations improve deployment speed, model reliability, reproducibility, monitoring, collaboration, and the long-term management of machine learning systems.
