Machine learning operations (MLOps) are practices for deploying and maintaining machine learning models in production. By using these practices, teams that manage the machine learning lifecycle, such as operations professionals, data scientists, and IT, will collaborate and communicate more effectively.
MLOps is fairly new and derives its principles from DevOps—the practice of efficiently producing, deploying, and running software—but applied to machine learning projects.
MLOps are unique to each business since what works for one may not be the optimal plan for another. However, if done correctly, it will result in these advantages:
Various components of the ML pipeline require different best practices. Below are a few to keep in mind:
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