MLOps is a compound term that combines "machine learning" and "operations." The role of MLOps, then, is to provide a communication conduit between data scientists who work with machine learning data and the operations team that manages the project. To do so, MLOps applies the type of cloud-native applications used in DevOps to machine learning (ML) services, specifically continuous integration/continuous deployment (CI/CD). Although both ML and normal cloud-native apps are written in (ok, result in) software, there is more to ML services than just code. While cloud-native apps require source version control, automated unit-/load -testing, AB testing, and final deployment, MLOps uses a data pipeline, ML model training, and more complex deployment with special purpose logging-monitoring capabilities.