MLOps

January 14, 2023 | permanent

Operations #

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MLOps or ML Ops is a set of practices that aims to deploy and maintain machine learning models in production reliably and efficiently.
The word is a compound of “machine learning” and the continuous development practice of DevOps in the software field.
Machine learning models are tested and developed in isolated experimental systems. When an algorithm is ready to be launched, MLOps is practiced between Data Scientists, DevOps, and Machine Learning engineers to transition the algorithm to production systems.[2] Similar to DevOps or DataOps approaches, MLOps seeks to increase automation and improve the quality of production models, while also focusing on business and regulatory requirements.
While MLOps started as a set of best practices, it is slowly evolving into an independent approach to ML lifecycle management.