Effective Data Science Infrastructure: How to Make Data Scientists Productive

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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 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.

可在其他賣家處以較低價格購買，這些賣家可能不提供免費的Prime運送。

簡化數據科學基礎設施，為數據科學家提供從原型到生產的高效路徑。



在《有效的數據科學基礎設施》中，您將學習如何：



    設計提高生產力的數據科學基礎設施

    在雲端處理計算和編排

    將機器學習部署到生產環境

    監控和管理性能和結果

    將基於雲端的工具結合成一個有機的數據科學環境

    使用Metaflow、Conda和Docker開發可重現的數據科學項目

    為多個團隊和大型數據集設計複雜的應用程序

    自定義和擴展數據科學基礎設施



《有效的數據科學基礎設施：如何提高數據科學家的生產力》是一本實用指南，教您如何組建數據科學和機器學習應用的基礎設施。它揭示了Netflix和其他數據驅動型公司用於管理尖端數據基礎設施的流程。通過這本書，您將掌握可擴展的數據存儲、計算、實驗跟踪和編排技術，這些技術適用於各種形狀和大小的公司。您將學習如何通過現有的雲端基礎設施、一套開源軟件和Python的慣用方式提高數據科學家的生產力。



作者將本書的收益捐贈給支持女性和被邊緣化群體的慈善機構。

購買印刷版書籍將包含Manning Publications提供的PDF、Kindle和ePub格式的免費電子書。



關於技術

將原型項目發展為生產項目需要可靠的基礎設施。使用本書中強大的新技術和工具，您可以建立一個能夠適應任何組織的基礎設施堆棧，無論是初創企業還是大型企業。



關於本書

《有效的數據科學基礎設施》教您如何構建能夠加速數據科學家和他們的項目的數據管道和項目工作流程。基於Netflix數據操作的最新工具和概念，本書介紹了一種可自定義的基於雲端的模型開發和MLOps方法，您可以輕鬆適應到公司的特定需求上。當您推出這些實用的流程時，您的團隊在應用數據科學和機器學習解決各種業務問題時將產生更好、更快的結果。



內容包括



    在雲端處理計算和編排

    將基於雲端的工具結合成一個有機的數據科學環境

    使用Metaflow、AWS和Python數據生態系統開發可重現的數據科學項目

    設計需要大型數據集、模型和數據科學家團隊的複雜應用程序



讀者對象

對於熟悉Python的基礎設施工程師和工程思維的數據科學家。

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.

在Netflix，Ville Tuulos設計並建立了Metaflow，這是一個針對數據科學的全棧框架。目前，他是一家專注於數據科學基礎設施的初創公司的首席執行官。

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

目錄
1. 介紹資料科學基礎架構
2. 資料科學的工具鏈
3. 介紹 Metaflow
4. 使用計算層進行擴展
5. 實踐可擴展性和效能
6. 進入生產環境
7. 處理資料
8. 使用和操作模型
9. 全棧機器學習