Machine Learning Operations (MLOps)

Machine Learning Operations (MLOps or ML Ops) are practices that aim to deploy and maintain machine learning models in production reliably and efficiently.

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What Is Machine Learning Operations?

The word is a compound of "machine learning", and the development practices of DevOps in the software field are continuous.
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.

MLOps Approach

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.

MLOps applies to the entire lifecycle - from integrating with model generation (software development lifecycle, continuous integration/continuous delivery), orchestration, and deployment, to health, diagnostics, governance, and business metrics. According to Gartner, MLOps is a subset of ModelOps.

MLOps is focused on the operationalization of ML models, while ModelOps covers the operationalization of all types of AI models.

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