Unless you live a secluded life as a cave-dwelling hermit, you've heard of MLOps, and you probably have, at the very least, an idea of what it is. For the cave-dwelling hermits out there, MLOps is a collection of procedures, implementations, and practices for machine learning model deployment and life cycle maintenance. If you are familiar with DevOps -- a similar approach for the continuous development of software -- you will undoubtedly note that MLOps is a portmanteau of machine Learning (ML) and the very same'Ops' from DevOps. With Machine Learning Model Operationalization Management (MLOps), we want to provide an end-to-end machine learning development process to design, build and manage reproducible, testable, and evolvable ML-powered software. You can read more about MLOps principles here.