Kubeflow Operations Guide: Managing Cloud and On-Premise Deployment
Patterson, Josh, Katzenellenbogen, Michael, Harris, Austin
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商品描述
Building models is a small part of the story when it comes to deploying machine learning applications. The entire process involves developing, orchestrating, deploying, and running scalable and portable machine learning workloads--a process Kubeflow makes much easier. This practical book shows data scientists, data engineers, and platform architects how to plan and execute a Kubeflow project to make their Kubernetes workflows portable and scalable.
Authors Josh Patterson, Michael Katzenellenbogen, and Austin Harris demonstrate how this open source platform orchestrates workflows by managing machine learning pipelines. You'll learn how to plan and execute a Kubeflow platform that can support workflows from on-premises to cloud providers including Google, Amazon, and Microsoft.
- Dive into Kubeflow architecture and learn best practices for using the platform
- Understand the process of planning your Kubeflow deployment
- Install Kubeflow on an existing on-premise Kubernetes cluster
- Deploy Kubeflow on Google Cloud Platform, AWS, and Azure
- Use KFServing to develop and deploy machine learning models
商品描述(中文翻譯)
建立模型只是部署機器學習應用的一小部分。整個過程包括開發、協調、部署和運行可擴展和可移植的機器學習工作負載，而Kubeflow可以使這個過程更加容易。這本實用書向數據科學家、數據工程師和平台架構師展示了如何計劃和執行Kubeflow項目，使他們的Kubernetes工作流程具有可移植性和可擴展性。
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流程來協調工作流程。您將學習如何計劃和執行一個Kubeflow平台，該平台可以支持從本地部署到包括Google、Amazon和Microsoft在內的雲提供商的工作流程。
深入了解Kubeflow架構，並學習使用該平台的最佳實踐。
了解計劃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。
作者簡介
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.
作者簡介(中文翻譯)
Josh Patterson是Patterson Consulting的首席執行官，該公司是一家在大數據和應用機器學習交叉領域的解決方案整合商。在這個角色中，他將他獨特的觀點融合了十年的大數據經驗和廣泛的深度學習經驗，應用於財富500項目。在Tennessee Valley Authority（TVA），Josh推動了Apache Hadoop的整合，用於大規模數據存儲和處理智能電網相位測量單元（PMU）數據。離開TVA後，Josh成為一家名為Cloudera（CLDR）的年輕Hadoop初創公司的首席解決方案架構師，作為第34位員工。離開Cloudera後，Josh共同創辦了Deeplearning4j項目，並與他人合著了《Deep Learning: A Practitioner's Approach》（O'Reilly Media）。
Michael Katzenellenbogen是一位獨立顧問，擁有深厚而廣泛的技術背景和經驗。他在年輕時就有幸接觸到技術，見證了互聯網的誕生及其各種轉變和階段。與互聯網一起成長使他能夠熟練掌握尖端技術。Michael在數據管理、軟件架構和利用新興技術創造和創新的方式方面具有深厚的背景。他曾在紐約時報管理數據，早期利用Hadoop等大數據平台，並在Cloudera擔任首席解決方案架構師的角色，幫助F100企業架構和實施非常大的數據和計算集群。Michael目前的重點是幫助企業降低機器學習的門檻，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。他通過與公司的諮詢合作，應用自己的知識來設計數據管道，以實時處理和分析大數據。他曾在智慧城市基礎設施、可穿戴技術和信號處理等領域工作。Austin在田納西大學查塔努加分校獲得計算機科學碩士學位。在就讀田納西大學期間，Austin發表了有關機器學習活動識別技術、HIPAA合規架構和實時動態路由算法的研究。
