Machine learning (ML) has matured into a mainstream development activity, and data scientists are expected to be able to write production-grade training pipelines. This course will provide you with a foundation in ML pipeline development guided by best practices in software engineering.

You’ll start by learning about code organization, style, and conceptual ideas, such as topological sorting of directed acyclic graphs. You’ll dive into the hands-on development of an ML pipeline. You’ll learn some advanced Python concepts and useful libraries. Finally, you’ll learn about testing methodologies and how to package your code into a distributable library.

By the end of this course, you’ll understand what makes a great ML pipeline work, better appreciate software engineering processes, improve your Python knowledge, and learn how to build ML training pipelines. This knowledge will make you more attractive to potential employers and improve your chances of thriving as a new data scientist.