This document outlines a presentation on revamping machine learning pipelines with MLOps. The presentation covers the machine learning lifecycle and challenges with ML productization. It provides examples of end-to-end ML platforms like Uber's Michaelangelo and Google's TFX. The presentation discusses MLOps best practices and methodologies, including build, retrain and release pipelines. It demonstrates MLflow and shows demos of Airflow, Tensorflow model serving, and TFX-based MLOps systems on Google Cloud and Azure.