Follow this series to learn about end-to-end ML project building, starting from data analysis to model serving into the cloud. The series shall serve as a pathway to a full-stack data scientist role in which readers will get to know some essential topics such as docker, flask, Heroku, CI/CD pipeline, unit testing, and many others. — A successful Machine Learning (ML) project involves several steps such as gathering data, data preparation, data exploration, feature engineering, model building, and serving out predictions to the end-users. …