What is Machine Learning Operations

A set of culture and practice that aims at unifying ML system development and operations, automate and monitoring all steps including integration, testing, releasing, deployment, and infrastructure management.

Wikipedia said

A set of practices that aims to deploy and maintain machine learning models in production reliably and efficiently

DataBricks said

MLOps stands for Machine Laerning Operations. MLOps is a core function of Machine Learning engineering, focused on streamlining the process of taking machine learning models to production, and then maintaining and monitoring them. MLOps is a collaborative function, often comprising data scientist, devops engineers, and IT.

Google said

MLOps is an ML engineering culture and practice that aims at unifying ML system development (Dev) and ML system operations (Ops). Practicing MLOps means that you advocate for automation and monitoring at all steps of ML system construction, including integration, testing, releasing, deployment and infrastructure management.

Algorithmia said

MLOps is the discipling of delivering machine learning (ML) models through repeatable and efficient workflows. MLOps enables the continuous delivery of high-performing ML applications into production at scale.

Neptune.ai said

MLOps is a set of practices for collaboration and communication between data scientist and operations professionals. Applying these practices increases the quality, simplifies the management process, and automates the deployment of Machine Learning and Deep Learning models in large-scale production environments. It's easier to align models with business needs, as well as regulartory requirements.

Nvidia said

MLOps is a set of best practices for businesses to run AI successfully.

MLOps.org said

The term MLOps defined as "the extension of the DevOps methodology to include Machine Learning and Data Science assets as first-class citizens within the DevOps ecology"