What is MLOps?

1. MLOps is an engineering discipline that aims to unify ML systems development (dev) and ML systems deployment (ops) in order to standardize and streamline the continuous delivery of high-performing models in production.
   what-is-mlops-machine-learning-operations-explained

2. This gives rise to the concept of MLOps, which follows a similar patter of DevOps. In other words, MLOps is a framework for collaboration between data scientists and the operations or production team. It is designed to get rid of waste, reduce errors, automate extensively, and produce richer, more consistent insights with machine learning. It is also a practice that drives a seamless integration between the development cycle and the overall operations process. It can likewise change how an association handles enormous information. Just like DevOps shortens production life cycles by creating better products with each iteration, MLOps drives insights you can trust and put into play more rapidly.
   https://medium.com/data-science-community-srm/the-future-of-machine-learning-mlops-b6116b84eb07

3. MLOps is a set of practices for managing and streamlining machine learning (ML) models’ lifecycle — from developement all the way to production.
   MLOps is not a platform, it is not a tooling, it is not a single process — it is an entire system or culture of how to productionalize machine learning models / data science solutions. And the better this system is, the faster and more robust the productionalization of the machine learning solutions is.

https://medium.com/datasentics/mlops-series-part-2-how-we-understand-mlops-at-datasentics-af5a0b26ce24