MLOps, which stands for Machine Learning Operations, is a practice that amalgamates machine learning (ML) and development operations (DevOps) to standardize and streamline the entire machine learning lifecycle. This lifecycle encompasses the development, deployment, and monitoring of machine learning models in production environments. MLOps aims to create a collaborative environment involving data scientists, DevOps engineers, and IT teams to ensure the smooth transition of ML models from development to production, and their subsequent monitoring and maintenance.
The core objective of MLOps is to automate and simplify the ML lifecycle, making the development and deployment processes more reliable, efficient, and productive. This is achieved by managing the entire lifecycle of a machine learning model, which includes training, tuning, deploying, and retiring models. It's a paradigm that not only aims at deploying and maintaining ML models in production reliably, but also at ensuring that the models are developed and tested in isolated, controlled settings before being deployed. MLOps incorporates practices, processes, and tools designed to improve collaborations between teams managing the ML lifecycle, akin to a well-coordinated assembly line in manufacturing. The integration of machine learning with DevOps practices within MLOps facilitates the continuous delivery of high-performing models in production, ensuring that ML systems are robust and maintainable over time.