Machine learning and statistics with python

 Prelogue: Recently kdNuggets published this poll survey depicting that one of the hottest skills is MLOps nowadays. Although I knew ML Ops is devOps for ML; but knowing that one line is clearly lacking as a data scientist. And boy that's not good for my career. So buckle your seats as I take you through my internet voyage through knowing basics of ML Ops. Introduction: To begin with, one must read the awesome intro to ML Ops written by google clouds. This awesome article first introduces the concept of CI/CD/CT. which are respectively continuous integration, continuous delivery and the last option being specific to ml models: continuous training.  This post then goes on to define each of terms and establishes the facts that ml code is actually a very small part of a data science exercise; with starting from product designing and data storage; to data validation, data fetching, model deployment and many more concepts actually come together to create the whole picture of ml...