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Description
Machine Learning Operations is referred to as MLOps. Therefore, the function of MLOps is to act as a communication link between the operations team overseeing the project and the data scientists who deal with machine learning data.
One significant difference between DevOps and MLOps is that ML services require data–and lots of it. In order to be suitable for ML model training, most data has to be cleaned, verified, and tagged. Much of this can be done in a stepwise fashion, as a data pipeline, where unclean data enters the pipeline, and then the training, validating, and testing data exits the pipeline.

Description
Machine Learning Operations is referred to as MLOps. Therefore, the function of MLOps is to act as a communication link between the operations team overseeing the project and the data scientists who deal with machine learning data.
One significant difference between DevOps and MLOps is that ML services require data–and lots of it. In order to be suitable for ML model training, most data has to be cleaned, verified, and tagged. Much of this can be done in a stepwise fashion, as a data pipeline, where unclean data enters the pipeline, and then the training, validating, and testing data exits the pipeline.