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.

  • 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.
  • 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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