Introduction
To gain consistent benefits from machine learning models in business, it is essential to move data science projects from experimentation to production by building automated machine learning pipelines. A standard machine learning pipeline consists of data preparation, model training, model evaluation and validation. The tasks of the automated pipeline range from collecting real-time streaming data to model and output management.
In this course, you will learn the life cycle of a data science project and the responsibilities of different roles in a data science team. You will also learn how to build an efficient end-to-end data science project and get hands-on experience on programming in python. Particular focus will be put on what is typically the most time consuming part, namely data curation, cleaning, and management, including data verification and testing.