BOOKS - PROGRAMMING - Distributed Machine Learning Patterns (MEAP v7)
Distributed Machine Learning Patterns (MEAP v7) - Yuan Tang 2023 PDF | EPUB Manning Publications BOOKS PROGRAMMING
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Distributed Machine Learning Patterns (MEAP v7)
Author: Yuan Tang
Year: 2023
Format: PDF | EPUB
File size: 18.6 MB
Language: ENG



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Book Description: Distributed Machine Learning Patterns MEAP v7 is a comprehensive guide to scaling Machine Learning (ML) systems on distributed Kubernetes clusters in the cloud. The book is filled with practical patterns for addressing common challenges faced by data analysts, data scientists, and software engineers who are familiar with the basics of ML algorithms and running ML in production. The book provides real-world scenarios that demonstrate how to apply each pattern, along with the potential trade-offs for each approach. The book begins by discussing the importance of understanding the process of technological evolution and developing a personal paradigm for perceiving the technological process of developing modern knowledge as the basis for human survival and unity in a warring state. It emphasizes the need to study and understand the process of technology evolution to stay relevant and adapt to new advancements in the field. The book then delves into the practical patterns for scaling ML systems on distributed Kubernetes clusters in the cloud. These patterns include supporting distributed model training, handling unexpected failures, and dynamic model serving traffic. Each pattern is designed to help solve common challenges faced when building distributed ML systems, and readers will learn how to apply these patterns in real-world scenarios. One of the key themes of the book is the need to develop a personal paradigm for perceiving the technological process of developing modern knowledge. This involves understanding the underlying principles and concepts of ML and how they are evolving over time.
Шаблоны распределенного машинного обучения MEAP v7 - это всеобъемлющее руководство по масштабированию систем машинного обучения (ML) на распределенных кластерах Kubernetes в облаке. Книга наполнена практическими шаблонами для решения общих проблем, с которыми сталкиваются аналитики данных, специалисты по анализу данных и инженеры программного обеспечения, знакомые с основами алгоритмов ML и работающие с ML в производстве. В книге представлены реальные сценарии, которые демонстрируют, как применять каждый шаблон, а также потенциальные компромиссы для каждого подхода. Книга начинается с обсуждения важности понимания процесса технологической эволюции и выработки личностной парадигмы восприятия технологического процесса развития современного знания как основы выживания и единства человека в воюющем государстве. Он подчеркивает необходимость изучения и понимания процесса эволюции технологий, чтобы оставаться актуальным и адаптироваться к новым достижениям в этой области. Затем книга углубляется в практические закономерности масштабирования ML-систем на распределенных кластерах Kubernetes в облаке. Эти шаблоны включают поддержку обучения распределенной модели, обработку непредвиденных сбоев и динамическую модель, обслуживающую трафик. Каждый шаблон предназначен для решения общих проблем, возникающих при создании распределенных ML-систем, и читатели узнают, как применять эти шаблоны в реальных сценариях. Одна из ключевых тем книги - необходимость выработки личностной парадигмы восприятия технологического процесса развития современных знаний. Это включает в себя понимание основных принципов и концепций ML и того, как они развиваются с течением времени.
Modèles d'Apprentissage Machine Distribué MEAP v7 est un guide complet de mise à l'échelle des systèmes d'Apprentissage Automatique (ML) sur les clusters Kubernetes distribués dans le cloud. livre est rempli de modèles pratiques pour résoudre les problèmes communs rencontrés par les analystes de données, les analystes de données et les ingénieurs logiciels familiers avec les bases des algorithmes ML et travaillant avec ML dans la production. livre présente des scénarios réels qui montrent comment appliquer chaque modèle, ainsi que des compromis potentiels pour chaque approche. livre commence par discuter de l'importance de comprendre le processus d'évolution technologique et de développer un paradigme personnel de la perception du processus technologique du développement de la connaissance moderne comme base de la survie et de l'unité de l'homme dans un État en guerre. Il souligne la nécessité d'étudier et de comprendre le processus d'évolution des technologies afin de rester pertinent et de s'adapter aux nouvelles avancées dans ce domaine. livre explore ensuite les schémas pratiques de mise à l'échelle des systèmes ML sur les clusters Kubernetes distribués dans le cloud. Ces modèles comprennent la prise en charge de l'apprentissage du modèle distribué, la gestion des pannes imprévues et un modèle dynamique de trafic. Chaque modèle est conçu pour résoudre les problèmes communs rencontrés lors de la création de systèmes ML distribués, et les lecteurs apprennent à appliquer ces modèles dans des scénarios réels. L'un des principaux thèmes du livre est la nécessité d'élaborer un paradigme personnel de la perception du processus technologique du développement des connaissances modernes. Cela implique de comprendre les principes et les concepts de base de ML et comment ils évoluent au fil du temps.
plantillas de aprendizaje automático distribuido MEAP v7 son una guía completa para escalar sistemas de aprendizaje automático (ML) en clústeres distribuidos de Kubernetes en la nube. libro está lleno de plantillas prácticas para resolver problemas comunes que enfrentan analistas de datos, analistas de datos e ingenieros de software familiarizados con los fundamentos de los algoritmos ML y trabajando con ML en la producción. libro presenta escenarios reales que demuestran cómo aplicar cada plantilla, así como posibles compromisos para cada enfoque. libro comienza con una discusión sobre la importancia de entender el proceso de evolución tecnológica y de generar un paradigma personal de percepción del proceso tecnológico del desarrollo del conocimiento moderno como base de la supervivencia y unidad del hombre en un Estado en guerra. Destaca la necesidad de estudiar y entender el proceso de evolución de la tecnología para seguir siendo relevante y adaptarse a los nuevos avances en este campo. A continuación, el libro profundiza en los patrones prácticos para escalar sistemas ML en clústeres distribuidos de Kubernetes en la nube. Estas plantillas incluyen soporte para aprendizaje de modelos distribuidos, manejo de fallas imprevistas y un modelo dinámico que sirve al tráfico. Cada plantilla está diseñada para resolver los problemas comunes que surgen al crear sistemas ML distribuidos y los lectores aprenderán a aplicar estas plantillas en escenarios reales. Uno de los temas clave del libro es la necesidad de generar un paradigma personal para percibir el proceso tecnológico del desarrollo del conocimiento moderno. Esto incluye comprender los principios y conceptos básicos de ML y cómo evolucionan a lo largo del tiempo.
Os modelos de aprendizado de máquina distribuídos MEAP v7 são um guia abrangente para a escala dos sistemas de aprendizagem de máquinas (ML) em clusters Kubernetes distribuídos na nuvem. O livro é repleto de modelos práticos para resolver problemas comuns enfrentados por analistas de dados, especialistas em análise de dados e engenheiros de software que conhecem os fundamentos dos algoritmos ML e trabalham com ML em produção. O livro apresenta cenários reais que demonstram como aplicar cada modelo e compromissos potenciais para cada abordagem. O livro começa por discutir a importância de compreender o processo de evolução tecnológica e estabelecer um paradigma de percepção pessoal do processo tecnológico de desenvolvimento do conhecimento moderno como base para a sobrevivência e a unidade do homem num estado em guerra. Ele ressalta a necessidade de estudar e compreender a evolução da tecnologia para se manter relevante e se adaptar a novos avanços nesta área. Em seguida, o livro é aprofundado no padrão prático de escala dos sistemas ML nos clusters Kubernetes distribuídos na nuvem. Estes modelos incluem suporte para treinamento de modelo distribuído, tratamento de falhas inesperadas e um modelo dinâmico de tráfego. Cada modelo é projetado para resolver problemas comuns que surgem na criação de sistemas ML distribuídos, e os leitores aprendem como aplicar esses modelos em cenários reais. Um dos temas-chave do livro é a necessidade de criar um paradigma pessoal para a percepção do processo tecnológico de desenvolvimento do conhecimento moderno. Isso inclui compreender os princípios e conceitos básicos da ML e como eles evoluem ao longo do tempo.
I modelli di apprendimento automatico distribuito MEAP v7 sono una guida completa per la scalabilità dei sistemi di apprendimento automatico (ML) sui cluster Kubernets distribuiti sul cloud. Il libro è dotato di modelli pratici per risolvere i problemi comuni che gli analisti di dati, gli esperti di analisi dei dati e gli ingegneri software che conoscono le basi degli algoritmi ML e lavorano con ML in produzione. Il libro presenta scenari reali che dimostrano come applicare ogni modello e potenziali compromessi per ogni approccio. Il libro inizia discutendo l'importanza di comprendere l'evoluzione tecnologica e di sviluppare il paradigma della percezione personale del processo tecnologico dello sviluppo della conoscenza moderna come base per la sopravvivenza e l'unità dell'uomo in uno stato in guerra. Sottolinea la necessità di studiare e comprendere l'evoluzione della tecnologia per rimanere aggiornati e adattarsi ai nuovi progressi in questo campo. Il libro viene quindi approfondito negli schemi pratici di scalabilità dei sistemi ML sui cluster Kubernets distribuiti nel cloud. Questi modelli includono il supporto per l'apprendimento del modello distribuito, l'elaborazione di guasti imprevisti e un modello dinamico di manutenzione del traffico. Ogni modello è progettato per risolvere i problemi comuni che si verificano durante la creazione di sistemi ML distribuiti e i lettori impareranno come applicare questi modelli in scenari reali. Uno dei temi chiave del libro è la necessità di sviluppare un paradigma personale per la percezione del processo tecnologico dello sviluppo della conoscenza moderna. Ciò include la comprensione dei principi e dei concetti di base di ML e di come si evolvono nel tempo.
Distributed Machine arning Templates MEAP v7 ist ein umfassender itfaden zur Skalierung von Machine arning (ML) -Systemen auf verteilten Kubernetes-Clustern in der Cloud. Das Buch ist voll von praktischen Mustern, um häufige Probleme von Datenanalysten, Datenwissenschaftlern und Softwareingenieuren zu lösen, die mit den Grundlagen von ML-Algorithmen vertraut sind und mit ML in der Produktion arbeiten. Das Buch präsentiert reale Szenarien, die zeigen, wie man jede Vorlage anwendet, sowie potenzielle Kompromisse für jeden Ansatz. Das Buch beginnt mit einer Diskussion über die Bedeutung des Verständnisses des technologischen Evolutionsprozesses und der Entwicklung eines persönlichen Paradigmas für die Wahrnehmung des technologischen Prozesses der Entwicklung des modernen Wissens als Grundlage für das Überleben und die Einheit des Menschen in einem kriegführenden Staat. Er betont die Notwendigkeit, den Prozess der Technologieentwicklung zu studieren und zu verstehen, um relevant zu bleiben und sich an neue Fortschritte in diesem Bereich anzupassen. Das Buch taucht dann in die praktischen Muster der Skalierung von ML-Systemen auf verteilten Kubernetes-Clustern in der Cloud ein. Zu diesen Vorlagen gehören die Unterstützung für verteiltes Modelltraining, die Verarbeitung unerwarteter Fehler und ein dynamisches Modell, das den Datenverkehr bedient. Jede Vorlage wurde entwickelt, um häufige Probleme zu lösen, die beim Erstellen verteilter ML-Systeme auftreten, und die ser lernen, wie sie diese Vorlagen in realen Szenarien anwenden können. Eines der Schlüsselthemen des Buches ist die Notwendigkeit, ein persönliches Paradigma für die Wahrnehmung des technologischen Prozesses der Entwicklung des modernen Wissens zu entwickeln. Dies beinhaltet das Verständnis der grundlegenden Prinzipien und Konzepte von ML und wie sie sich im Laufe der Zeit entwickeln.
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Dağıtılmış Makine Öğrenimi Şablonları MEAP v7, buluttaki dağıtılmış Kubernetes kümelerinde makine öğrenimi (ML) sistemlerini ölçeklendirmek için kapsamlı bir kılavuzdur. Kitap, veri analistleri, veri bilimcileri ve ML algoritmalarının temellerini bilen ve üretimde ML ile çalışan yazılım mühendislerinin karşılaştığı ortak zorlukları ele almak için pratik şablonlarla doludur. Kitap, her bir şablonun nasıl uygulanacağını ve her bir yaklaşım için potansiyel ödünleşimleri gösteren gerçek dünya senaryolarını sunar. Kitap, teknolojik evrim sürecini anlamanın ve modern bilginin gelişiminin teknolojik sürecinin, savaşan bir durumdaki bir kişinin hayatta kalması ve birliği için temel olarak algılanması için kişisel bir paradigma geliştirmenin öneminin tartışılmasıyla başlar. İlgili kalmak ve alandaki yeni gelişmelere uyum sağlamak için teknolojinin evrim sürecini inceleme ve anlama ihtiyacını vurgulamaktadır. Kitap daha sonra, buluttaki dağıtılmış Kubernetes kümelerinde ML sistemlerini ölçeklendirmenin pratik kalıplarını inceliyor. Bu şablonlar dağıtılmış model eğitim desteği, beklenmedik arıza yönetimi ve trafiğe hizmet eden dinamik bir model içerir. Her şablon, dağıtılmış ML sistemleri oluştururken ortaya çıkan yaygın sorunları çözmek için tasarlanmıştır ve okuyucular bu şablonları gerçek dünya senaryolarında nasıl uygulayacaklarını öğreneceklerdir. Kitabın ana konularından biri, modern bilginin gelişiminin teknolojik sürecinin algılanması için kişisel bir paradigma geliştirme ihtiyacıdır. Bu, ML'nin temel ilkelerini ve kavramlarını ve zaman içinde nasıl geliştiklerini anlamayı içerir.
قوالب التعلم الآلي الموزعة MEAP v7 هي دليل شامل لتوسيع نطاق أنظمة التعلم الآلي (ML) على مجموعات Kubernetes الموزعة في السحابة. الكتاب مليء بقوالب عملية لمعالجة التحديات الشائعة التي يواجهها محللو البيانات وعلماء البيانات ومهندسو البرمجيات المطلعون على أساسيات خوارزميات ML والعمل مع ML في التصنيع. يقدم الكتاب سيناريوهات العالم الحقيقي التي توضح كيفية تطبيق كل نموذج، بالإضافة إلى المقايضات المحتملة لكل نهج. يبدأ الكتاب بمناقشة أهمية فهم عملية التطور التكنولوجي وتطوير نموذج شخصي لتصور العملية التكنولوجية لتطور المعرفة الحديثة كأساس لبقاء ووحدة شخص في حالة حرب. ويشدد على ضرورة دراسة وفهم عملية تطور التكنولوجيا من أجل الحفاظ على أهميتها والتكيف مع التطورات الجديدة في الميدان. ثم يتعمق الكتاب في الأنماط العملية لتحجيم أنظمة ML على مجموعات Kubernetes الموزعة في السحابة. تتضمن هذه القوالب دعم التدريب النموذجي الموزع، والتعامل مع الفشل غير المتوقع، ونموذج ديناميكي يخدم حركة المرور. تم تصميم كل قالب لحل المشكلات الشائعة التي تنشأ عند إنشاء أنظمة ML موزعة، وسيتعلم القراء كيفية تطبيق هذه القوالب في سيناريوهات العالم الحقيقي. أحد المواضيع الرئيسية للكتاب هو الحاجة إلى تطوير نموذج شخصي لتصور العملية التكنولوجية لتطوير المعرفة الحديثة. وهذا يشمل فهم المبادئ والمفاهيم الأساسية لـ ML وكيف تتطور بمرور الوقت.

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