Kubeflow Operations Guide: Managing Cloud and On-Premise Deployment
暫譯: Kubeflow 操作指南：管理雲端與本地部署

建立模型只是部署機器學習應用程式的一小部分。整個過程涉及開發、協調、部署和運行可擴展且可攜帶的機器學習工作負載——這個過程由 Kubeflow 大大簡化。本書實用地向數據科學家、數據工程師和平台架構師展示如何計劃和執行 Kubeflow 專案，以使他們的 Kubernetes 工作流程可攜帶且可擴展。

作者 Josh Patterson、Michael Katzenellenbogen 和 Austin Harris 演示了這個開源平台如何通過管理機器學習管道來協調工作流程。您將學習如何計劃和執行一個可以支持從本地到雲端提供商（包括 Google、Amazon 和 Microsoft）的工作流程的 Kubeflow 平台。

- 深入了解 Kubeflow 架構並學習使用該平台的最佳實踐
- 理解計劃您的 Kubeflow 部署的過程
- 在現有的本地 Kubernetes 集群上安裝 Kubeflow
- 在 Google Cloud Platform、AWS 和 Azure 上部署 Kubeflow
- 使用 KFServing 開發和部署機器學習模型

Josh Patterson is CEO of Patterson Consulting, a solution integrator at the intersection of big data and applied machine learning. In this role, he brings his unique perspective blending a decade of big data experience and wide-ranging deep learning experience to Fortune 500 projects. At the Tennessee Valley Authority (TVA), Josh drove the integration of Apache Hadoop for large-scale data storage and processing of smart grid phasor measurement unit (PMU) data. Post-TVA, Josh was a principal solutions architect for a young Hadoop startup named Cloudera (CLDR), as employee 34. After leaving Cloudera, Josh co-founded the Deeplearning4j project and co-wrote Deep Learning: A Practitioner's Approach (O'Reilly Media).

Michael Katzenellenbogen is an independent consultant with a deep and wide technological background and experience. He had the good fortune of getting involved with technology at a young age, and has been witness to the birth of the Internet and its various transformations and stages. Having grown up with and alongside the Internet has allowed him to become adept in cutting edge technologies. Michael has a deep background in data management, software architecture, and leveraging new and emerging technologies in creative and novel ways. His roles included managing data for The New York Times, leveraging big data platforms, such as Hadoop, early on, as well as in the role of Principal Solutions Architect at Cloudera, helping F100 enterprises architect and implement very large data and compute clusters. Michael's current focus is in helping enterprises lower the barrier to entry for Machine Learning, leveraging technologies such as Kubernetes and Kubeflow.

Austin Harris is a Distributed Systems Engineer based in Chattanooga, Tennessee. Austin is a specialist in Apache Kafka and distributed systems architecture. He has applied his knowledge via consulting with companies to architect data pipelines in order to handle and analyze big data in real-time. He has worked in fields including smart city infrastructure, wearable technologies, and signal processing. Austin received a master's degree in Computer Science from the University of Tennessee at Chattanooga. While attending the University of Tennessee Austin published research on machine learning activity recognition techniques, HIPAA compliant architectures, and real-time dynamic routing algorithms.

Josh Patterson 是 Patterson Consulting 的執行長，該公司是一家專注於大數據與應用機器學習交集的解決方案整合商。在這個角色中，他結合了十年的大數據經驗和廣泛的深度學習經驗，為《財富》500 強企業的專案提供獨特的視角。在田納西河谷管理局 (TVA) 任職期間，Josh 推動了 Apache Hadoop 的整合，以進行智慧電網相量測量單元 (PMU) 數據的大規模存儲和處理。在 TVA 之後，Josh 成為一家名為 Cloudera (CLDR) 的年輕 Hadoop 新創公司的首席解決方案架構師，擔任第 34 位員工。離開 Cloudera 後，Josh 共同創立了 Deeplearning4j 專案，並共同撰寫了《深度學習：實務者的方式》（O'Reilly Media）。

Michael Katzenellenbogen 是一位獨立顧問，擁有深厚且廣泛的技術背景和經驗。他在年輕時就有幸接觸到技術，並見證了互聯網的誕生及其各種轉變和階段。與互聯網共同成長使他能夠熟練掌握尖端技術。Michael 在數據管理、軟體架構方面有深厚的背景，並能以創新和新穎的方式利用新興技術。他的角色包括為《紐約時報》管理數據，早期利用大數據平台如 Hadoop，以及在 Cloudera 擔任首席解決方案架構師，幫助《財富》100 強企業架構和實施非常大的數據和計算集群。Michael 當前的重點是幫助企業降低機器學習的入門門檻，利用 Kubernetes 和 Kubeflow 等技術。

Austin Harris 是一位位於田納西州查塔努加的分散式系統工程師。Austin 是 Apache Kafka 和分散式系統架構的專家。他通過與公司合作，架構數據管道以實時處理和分析大數據，應用了他的知識。他的工作領域包括智慧城市基礎設施、可穿戴技術和信號處理。Austin 在田納西州查塔努加大學獲得計算機科學碩士學位。在就讀田納西州大學期間，Austin 發表了有關機器學習活動識別技術、符合 HIPAA 的架構和實時動態路由演算法的研究。