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MLOps

MLOps (a compound of “machine learning” and “operations”), a subset of ModelOps is a practice for collaboration and communication between data scientists and operations professionals to help manage production ML (or deep learning) lifecycle. Similar to the DevOps or DataOps approaches, MLOps looks to increase automation and improve the quality of production ML while also focusing on business and regulatory requirements. While MLOps also started as a set of best practices, it is slowly evolving into an independent approach to ML lifecycle management. 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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These are the most often used hashtags on social media when discussing MLOps. The top three related terms are mlops, ai, and machinelearning.

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Data Engineer

MLOps is most commonly found in Data Engineer job descriptions. To learn more about the role, click the button below.

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