MLOps stands for Machine Learning Operations. It is a set of practices that combines machine learning, DevOps, and IT operations to automate the end-to-end machine learning lifecycle, from data preparation to model deployment and monitoring. The goal of MLOps is to make it easier to deploy and maintain machine learning models in production, while ensuring that they are reliable and efficient. MLOps can help to improve the quality of machine learning models, reduce the time it takes to get them into production, and make it easier to scale machine learning applications.

Kubeflow is an open-source platform for machine learning and MLOps on Kubernetes. It provides a set of tools and components that make it easy to deploy, manage, and scale machine learning workflows on Kubernetes. Kubeflow includes a variety of components, including: Notebooks: A Jupyter notebook service that allows data scientists to develop and experiment with machine learning models. Pipelines: A tool for building and deploying machine learning pipelines. Experimentation: A tool for tracking and managing machine learning experiments.