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MLOps refers to the set of practices that help an organization deploy, monitor, and manage machine learning models with speed and precision. It encompasses the people, processes, and technologies involved in developing ML algorithms and putting them into production. Generally speaking, MLOps seeks to remove obstacles and unnecessary complexity from the machine learning process. The goal is to simplify processes and establish standards in order to improve the quality of ML models and the speed at which they can be brought into production.

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More Data Engineering Terms
ML Pipeline Data Drift Data Lineage
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ML Pipeline