Effective Data Science Infrastructure

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.

Simplify data science infrastructure to give data scientists an efficient path from prototype to production.

In Effective Data Science Infrastructure you will learn how to:

• Design data science infrastructure that boosts productivity
• Handle compute and orchestration in the cloud
• Deploy machine learning to production
• Monitor and manage performance and results
• Combine cloud-based tools into a cohesive data science environment
• Develop reproducible data science projects using Metaflow, Conda, and Docker
• Architect complex applications for multiple teams and large datasets
• Customize and grow data science infrastructure

Effective Data Science Infrastructure: How to make data scientists more productive 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. In it, you''ll master scalable techniques for data storage, computation, experiment tracking, and orchestration. You''ll also learn how to collaborate with data scientists to deliver exactly what they need to succeed.

The author is donating proceeds from this book to charities that support women and under represented groups in data science.