Machine learning engineering is the application of engineering techniques to the design, development, and deployment of machine learning systems. These systems use algorithms to analyze and learn from data, in order to make predictions or take actions based on that learning.
MLOps, or Machine Learning Operations, is the practice of integrating machine learning into the overall development and operation of a software system. It involves the collaboration of data scientists, machine learning engineers, and IT professionals in order to streamline the process of building, deploying, and maintaining machine learning models in production environments.
MLOps aims to improve the efficiency and effectiveness of machine learning in an organization by automating and streamlining the various processes involved in building and deploying machine learning models. This includes tasks such as model training, model deployment, and model monitoring. By adopting MLOps best practices, organizations can more easily and reliably incorporate machine learning into their operations, which can lead to improved decision making and a competitive advantage.