Kubeflow for Machine Learning: From Lab to Production
暫譯: Kubeflow 機器學習：從實驗室到生產環境

Grant, Trevor, Karau, Holden, Lublinsky, Boris

Kubeflow for Machine Learning: From Lab to Production

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商品描述

If you're training a machine learning model but aren't sure how to put it into production, this book will get you there. Kubeflow provides a collection of cloud native tools for different stages of a model's lifecycle, from data exploration, feature preparation, and model training to model serving. This guide helps data scientists build production-grade machine learning implementations with Kubeflow and shows data engineers how to make models scalable and reliable.

Using examples throughout the book, authors Holden Karau, Trevor Grant, Ilan Filonenko, Richard Liu, and Boris Lublinsky explain how to use Kubeflow to train and serve your machine learning models on top of Kubernetes in the cloud or in a development environment on-premises.

  • Understand Kubeflow's design, core components, and the problems it solves
  • Learn how to set up Kubeflow on a cloud provider or on an in-house cluster
  • Train models using Kubeflow with popular tools including scikit-learn, TensorFlow, and Apache Spark
  • Learn how to add custom stages such as serving and prediction
  • Keep your model up-to-date with Kubeflow Pipelines
  • Understand how to validate machine learning pipelines

商品描述(中文翻譯)

如果您正在訓練一個機器學習模型，但不確定如何將其投入生產，這本書將幫助您達成目標。Kubeflow 提供了一系列雲原生工具，涵蓋模型生命周期的不同階段，從數據探索、特徵準備、模型訓練到模型服務。本指南幫助數據科學家使用 Kubeflow 構建生產級的機器學習實現，並向數據工程師展示如何使模型具備可擴展性和可靠性。

作者 Holden Karau、Trevor Grant、Ilan Filonenko、Richard Liu 和 Boris Lublinsky 在全書中使用範例，解釋如何在雲端的 Kubernetes 上或在內部開發環境中使用 Kubeflow 訓練和服務您的機器學習模型。

- 了解 Kubeflow 的設計、核心組件及其解決的問題
- 學習如何在雲服務提供商或內部集群上設置 Kubeflow
- 使用 Kubeflow 訓練模型，並搭配流行工具如 scikit-learn、TensorFlow 和 Apache Spark
- 學習如何添加自定義階段，例如服務和預測
- 使用 Kubeflow Pipelines 使您的模型保持最新
- 了解如何驗證機器學習管道

作者簡介

Trevor Grant is a member of the Apache Software Foundation, and is heavily involved in the Apache Mahout, Apache Streams, and Community Development projects. He often tinkers and occasionally documents his (mis)adventures at www.rawkintrevo.org. In the before time, he was an international speaker on technology, but now he focuses mainly on writing. Trevor wishes to thank IBM for their continued patronage of his artistic endeavors. He lives in Chicago because it's the best city on the planet, with world class food, parks, and culture, and because the skies are never orange.

Holden Karau is a queer transgender Canadian, Apache Spark committer, Apache Software Foundation member, and an active open source contributor. She also extends her passion for building community with industry projects including Scaling for Python for ML and teaching distributed computing to children. As a software engineer, she's worked on a variety of distributed compute, search, and classification problems at Google, IBM, Alpine, Databricks, Foursquare, and Amazon. She graduated from the University of Waterloo with a bachelor of mathematics in computer science. Outside of software she enjoys playing with fire, welding, riding scooters, eating poutine, and dancing.

Boris Lublinsky is a Principal Architect at Lightbend. Boris has over 25 years experience in enterprise, technical architecture, and software engineering. He is an active member of OASIS SOA RM committee, co-author of Applied SOA: Service-Oriented Architecture and Design Strategies (Wiley) and author of numerous articles on Architecture, Programming, Big Data, SOA and BPM.

Richard Liu is a Senior Software Engineer at Waymo, where he focuses on building a machine learning platform for self-driving cars. Previously he has worked at Microsoft Azure and Google Cloud. He is one of the primary maintainers of the Kubeflow project and has given several talks at KubeCon. He holds a Master's degree in Computer Science from University of California, San Diego.

Ilan Filonenko is a member of the Data Science Infrastructure team at Bloomberg, where he has designed and implemented distributed systems at both the application and infrastructure level. Previously, Ilan was an engineering consultant and technical lead in various startups and research divisions across multiple industry verticals, including medicine, hospitality, finance, and music. He actively contributes to open source, primarily Apache Spark and Kubeflow's KFServing. He is one of the principal contributors to Spark on Kubernetes--primarily focusing on remote shuffle and HDFS security, and to multimodel serving in KFServing. Ilan's research has been in algorithmic, software, and hardware techniques for high-performance machine learning with a focus on optimizing stochastic algorithms and model management.

作者簡介(中文翻譯)

Trevor Grant 是 Apache 軟體基金會的成員，並積極參與 Apache Mahout、Apache Streams 和社群發展專案。他經常進行實驗，並偶爾在 www.rawkintrevo.org 上記錄他的（不幸）冒險。在過去，他曾是國際技術演講者，但現在主要專注於寫作。Trevor 想感謝 IBM 對他藝術創作的持續支持。他住在芝加哥，因為那是地球上最好的城市，擁有世界級的美食、公園和文化，並且天空從不呈橙色。

Holden Karau 是一位酷兒跨性別的加拿大人，Apache Spark 的提交者，Apache 軟體基金會的成員，以及活躍的開源貢獻者。她還將她對社群建設的熱情擴展到行業專案，包括為機器學習的 Python 擴展和教導兒童分散式計算。作為一名軟體工程師，她曾在 Google、IBM、Alpine、Databricks、Foursquare 和 Amazon 等公司處理各種分散式計算、搜尋和分類問題。她畢業於滑鐵盧大學，獲得計算機科學的數學學士學位。在軟體之外，她喜歡玩火、焊接、騎滑板車、吃 poutine 和跳舞。

Boris Lublinsky 是 Lightbend 的首席架構師。Boris 擁有超過 25 年的企業、技術架構和軟體工程經驗。他是 OASIS SOA RM 委員會的活躍成員，《Applied SOA: Service-Oriented Architecture and Design Strategies》（Wiley）的共同作者，以及多篇有關架構、程式設計、大數據、SOA 和 BPM 的文章的作者。

Richard Liu 是 Waymo 的高級軟體工程師，專注於為自駕車構建機器學習平台。之前他曾在 Microsoft Azure 和 Google Cloud 工作。他是 Kubeflow 專案的主要維護者之一，並在 KubeCon 上發表過幾次演講。他擁有加州大學聖地牙哥分校的計算機科學碩士學位。

Ilan Filonenko 是 Bloomberg 數據科學基礎設施團隊的成員，他在應用和基礎設施層面設計和實施了分散式系統。之前，Ilan 是各種初創公司和研究部門的工程顧問和技術負責人，涉及醫療、酒店、金融和音樂等多個行業。他積極貢獻於開源，主要是 Apache Spark 和 Kubeflow 的 KFServing。他是 Spark on Kubernetes 的主要貢獻者之一，主要專注於遠程洗牌和 HDFS 安全，以及 KFServing 中的多模型服務。Ilan 的研究集中在高效能機器學習的算法、軟體和硬體技術上，特別是優化隨機算法和模型管理。

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