MLOps, an abbreviation for Machine Learning Operations, is a set of practices and processes for managing the end-to-end lifecycle of machine learning models. It includes tasks such as model development, testing, deployment, monitoring, and maintenance. The goal is to ensure the reliable and efficient delivery of machine learning models into production.

MLOps aims to address some of the challenges associated with deploying and maintaining machine learning models in production, such as model governance, model monitoring, model deployment, automating the deployment of models into production, and updating them as needed, and model lifecycle management: