MLOps, short for Machine Learning Operations, is a paradigm that aims to deploy and maintain machine learning models in production reliably and efficiently. It is a core function of machine learning engineering, focused on streamlining the process of taking machine learning models to production. MLOps applies to the entire lifecycle of machine learning, from integrating with model generation, orchestration, and deployment, to health, diagnostics, governance, and business metrics.

MLOps is aimed at productionizing machine learning systems by bridging the gap between development (Dev) and operations (Ops) . Essentially, MLOps aims to facilitate the creation of machine learning products by leveraging principles such as CI/CD automation, workflow orchestration, reproducibility, versioning of data, model, and code, collaboration, continuous ML training and evaluation, ML metadata tracking and logging, continuous monitoring, and feedback loops.

MLOps is a set of engineering practices specific to machine learning projects that borrow from the more widely-adopted DevOps principles in software engineering. While DevOps brings a rapid, continuously iterative approach to shipping applications, MLOps borrows the same principles to take machine learning models to production. The outcome of MLOps is higher software quality, faster patching and releases, and higher customer satisfaction.

MLOps is an emerging field that is rapidly gaining momentum amongst data scientists, machine learning engineers, and AI enthusiasts. It is slowly evolving into an independent approach to the machine learning lifecycle that includes all steps, from data gathering to governance and monitoring. MLOps is a useful approach for the creation and quality of machine learning and AI solutions, and it will become a standard as artificial intelligence is moving towards becoming part of everyday business, rather than just an innovative activity.