Machine Learning Operations (MLOps)

Machine Learning Operations (MLOps) refers to the set of practices and tools that facilitates the deployment, management, and monitoring of machine learning models in production environments. MLOps integrates machine learning systems with operations and aims to streamline the workflow between data scientists and operations teams, ensuring that models can be efficiently developed, deployed, and maintained over time. It encompasses various activities such as version control of data and models, automation of model training and deployment processes, continuous integration and delivery of machine learning pipelines, and monitoring model performance in real-time. MLOps also involves establishing governance and compliance measures to ensure that machine learning applications adhere to organizational standards and ethical guidelines. The goal of MLOps is to enhance collaboration, reduce errors, and improve the overall speed and quality of machine learning initiatives within an organization.