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Effective Data Science Infrastructure - by Ville Tuulos (Paperback)
About this item
Highlights
- Simplify data science infrastructure to give data scientists an efficient path from prototype to production.
- About the Author: Ville Tuulos has been developing tools and infrastructure for data science and machine learning for over two decades.
- 352 Pages
- Computers + Internet, Software Development & Engineering
Description
About the Book
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.
Book Synopsis
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 productivityHandle 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 that are 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. The author is donating proceeds from this book to charities that support women and underrepresented groups in data science. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology
Growing data science projects from prototype to production requires reliable infrastructure. Using the powerful new techniques and tooling in this book, you can stand up an infrastructure stack that will scale with any organization, from startups to the largest enterprises. About the book
Effective Data Science Infrastructure teaches you to build data pipelines and project workflows that will supercharge data scientists and their projects. Based on state-of-the-art tools and concepts that power data operations of Netflix, this book introduces a customizable cloud-based approach to model development and MLOps that you can easily adapt to your company's specific needs. As you roll out these practical processes, your teams will produce better and faster results when applying data science and machine learning to a wide array of business problems. What's inside Handle compute and orchestration in the cloud
Combine cloud-based tools into a cohesive data science environment
Develop reproducible data science projects using Metaflow, AWS, and the Python data ecosystem
Architect complex applications that require large datasets and models, and a team of data scientists About the reader
For infrastructure engineers and engineering-minded data scientists who are familiar with Python. About the author
At Netflix, Ville Tuulos designed and built Metaflow, a full-stack framework for data science. Currently, he is the CEO of a startup focusing on data science infrastructure. Table of Contents
1 Introducing data science infrastructure
2 The toolchain of data science
3 Introducing Metaflow
4 Scaling with the compute layer
5 Practicing scalability and performance
6 Going to production
7 Processing data
8 Using and operating models
9 Machine learning with the full stack
From the Back Cover
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.
Review Quotes
"Do not miss the opportunity to cover all key aspects of data science infrastructure on your next project." Jesús A. Juárez Guerrero
"Useful book that provides tactical guidance on how to use Metaflow to streamline data science workflows but also includes great frameworks and abstractions to consider when defining your data science infrastructure stack." Sarah Catanzaro
"This is the ultimate book to learn how to handle infrastructure in data science!" Ninoslav Cerkez
"If you need a workflow management tool to glue your data code, look at metaflow. It's simple yet efficient." Mikael Dautrey
About the Author
Ville Tuulos has been developing tools and infrastructure for data science and machine learning for over two decades. At Netflix, he designed and built Metaflow, a full-stack framework for data science. Currently, he is the CEO of a startup focusing on data science infrastructure.