Effective Data Science Infrastructure: How to make data scientists more productive is a guide to building infrastructure that will supercharge data science projects and data scientists. Based on state-of-the-art practices that power the massive data operations of Netflix, this book offers techniques and patterns relevant to companies of all shapes and sizes. You'll learn how you can make data scientists more productive with your existing cloud infrastructure, a stack of open source software, and idiomatic Python.

As you work through this easy-to-follow guide, you'll set up end-to-end infrastructure from the ground up, with a fully customizable process you can easily adapt to your company. You'll build a cloud-based development environment that covers local prototyping and deployment to production, set up infrastructure that supports a real-world machine learning application, and handle a large-scale application for processing hundreds of gigabytes of data. Throughout, you'll follow a human-centric approach focused on user experience and meeting the unique needs of data scientists.

Effective Data Science Infrastructure is a hands-on guide to assembling infrastructure for data science and machine learning applications. It reveals the processes used at Netflix and other data driven companies to manage their cutting edge data infrastructure.

As you work through this easy-to-follow guide, you'll set up end-to end infrastructure from the ground up, with a fully customizable process you can easily adapt to your company. You'll learn how you can make data scientists more productive with your existing cloud infrastructure, a stack of open source software, and idiomatic Python. Throughout, you'll follow a human-centric approach focused on user experience and meeting the unique needs of data scientists.

About the Technology
Turning data science projects from small prototypes to sustainable business processes requires scalable and reliable infrastructure. This book lays out the workflows, components, and methods of the full infrastructure stack for data science, from data warehousing and scalable compute to modeling frameworks.