Course Overview

Putting predictive models into production is one of the most direct ways that data scientists can add value to an organization. Data scientists can own more of the model production process and rapidly deliver data products by learning how to build and deploy scalable model pipelines. Building data products is more than just putting code into production, it also includes DevOps and lifecycle management of live systems.

Purpose of this course

Throughout this course, we’ll cover different cloud environments and tools for building scalable data and model pipelines. The goal is to provide readers with the opportunity to get hands-on experience and start building with a number of different tools. Since this course is targeted at analytics practitioners with prior Python experience, we’ll walk through examples from start to finish but won’t dig into the details of the programming language itself.