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Machine Learning Design Patterns Solutions to Common Challenges in Data Preparation, Model Building, and MLOps, First Edition - Valliappa Lakshmanan, Sara Robinson, and Michael Munn 2020-10-15 EPUB O’Reilly Media, Inc BOOKS PROGRAMMING
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Machine Learning Design Patterns Solutions to Common Challenges in Data Preparation, Model Building, and MLOps, First Edition
Author: Valliappa Lakshmanan, Sara Robinson, and Michael Munn
Year: 2020-10-15
Format: EPUB
File size: 16.7 MB
Language: ENG



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Book Description: Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps (First Edition) Authors: Three Google Engineers (Names not provided) Publisher: O'Reilly Media Publication Date: April 2019 Pages: 368 pages Genre: Technology, Artificial Intelligence, Machine Learning Summary: Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps is a comprehensive guide that provides practical solutions to recurring challenges faced by data scientists and machine learning practitioners. The book captures best practices and proven methods to help tackle common problems throughout the ML process, from data preparation to model building and deployment.
Шаблоны проектирования машинного обучения: решения распространенных проблем при подготовке данных, построении моделей и MLOps (первое издание) Авторы: Три инженера Google (имена не указаны) Издатель: O'Reilly Media Дата публикации: Апрель 2019 г. Страницы: 368 страниц Жанр: технологии, искусственный интеллект, машинное обучение Резюме: Шаблоны проектирования машинного обучения: решения общих проблем в подготовке данных, построении моделей и MLOps - это комплексное руководство, которое предоставляет практические решения повторяющихся проблем, с которыми сталкиваются специалисты по анализу данных и практики машинного обучения. В книге собраны передовые практики и проверенные методы, помогающие решать общие проблемы на протяжении всего процесса ML - от подготовки данных до построения и развертывания моделей.
Modèles de conception d'apprentissage automatique : solutions aux problèmes courants dans la préparation des données, la construction de modèles et MLOps (première édition) Auteurs : Trois ingénieurs Google (noms non indiqués) Éditeur : O'Reilly Media Date de publication : Avril 2019 Pages : 368 pages Genre : technologie, intelligence artificielle, apprentissage automatique Résumé : Modèles de conception Machine arning : solutions aux problèmes communs dans la préparation des données, la modélisation et les MLOps est un guide complet qui fournit des solutions pratiques aux problèmes récurrents rencontrés par les spécialistes de l'analyse des données et des pratiques Machine arning. livre rassemble les meilleures pratiques et les méthodes éprouvées pour aider à résoudre les problèmes communs tout au long du processus ML - de la préparation des données à la construction et au déploiement des modèles.
Plantillas de diseño de aprendizaje automático: soluciones a problemas comunes en la preparación de datos, construcción de modelos y MLOps (primera edición) Autores: Tres ingenieros de Google (nombres no especificados) Editor: O'Reilly Media Fecha de publicación: Abril 2019 Páginas: 368 páginas Género: tecnología, inteligencia artificial, aprendizaje automático Resumen: Plantillas de diseño de aprendizaje automático: soluciones a problemas comunes en la preparación de datos, construcción de modelos y MLOps es una guía integral que proporciona soluciones prácticas a los problemas recurrentes que enfrentan los especialistas en análisis de datos y prácticas de aprendizaje automático. libro recoge buenas prácticas y métodos probados para ayudar a resolver problemas comunes a lo largo del proceso de ML, desde la preparación de datos hasta la construcción e implementación de modelos.
Modelos de projeto de aprendizado de máquina: soluções para problemas comuns na produção de dados, construção de modelos e MLOps (primeira edição) Autores: Três engenheiros do Google (nomes não especificados) Editor: O'Reilly Media Data de publicação: Abril de 2019 Páginas: 368 páginas Gênero: tecnologia, inteligência artificial, aprendizagem de máquinas Resumo: Modelos de projeto de aprendizado de máquina: resolver problemas comuns de produção de dados, modelagem e MLOps é um guia completo que oferece soluções práticas para os problemas recorrentes enfrentados por especialistas em análise de dados e práticas de aprendizagem automática. O livro reúne as melhores práticas e técnicas testadas que ajudam a resolver problemas comuns durante todo o processo ML, desde a produção de dados até a construção e implantação de modelos.
Modelli di progettazione di apprendimento automatico: soluzioni ai problemi comuni nella preparazione dei dati, nella progettazione dei modelli e in MLOps (prima edizione) Autori: Tre ingegneri di Google (nessun nome) Editore: O'Reilly Media Data di pubblicazione: Aprile 2019 Pagine: 368 pagine Genere: tecnologia, intelligenza artificiale, apprendimento automatico Riepilogo: Modelli di progettazione per l'apprendimento automatico: le soluzioni ai problemi comuni di elaborazione dei dati, modellazione e MLOps sono una guida completa che fornisce soluzioni pratiche ai problemi ricorrenti di analisi dei dati e apprendimento automatico. Il libro contiene procedure ottimali e metodi collaudati che consentono di risolvere i problemi generali durante tutto il processo ML, dalla preparazione dei dati alla creazione e all'installazione dei modelli.
Machine arning Design Templates: Lösungen für häufige Probleme bei Datenvorbereitung, Modellbau und MLOps (Erstausgabe) Autoren: Drei Google-Ingenieure (keine Namen angegeben) Herausgeber: O'Reilly Media Veröffentlichungsdatum: April 2019 Seiten: 368 Seiten Genre: Technologie, Künstliche Intelligenz, Maschinelles rnen Zusammenfassung: Machine arning Design Patterns: Lösungen für häufige Probleme in der Datenvorbereitung, Modellbau und MLOps ist ein umfassender itfaden, der praktische Lösungen für wiederkehrende Probleme bietet, mit denen Datenwissenschaftler und Machine arning-Praktiken konfrontiert sind. Das Buch enthält Best Practices und bewährte Methoden, die helfen, häufige Probleme während des gesamten ML-Prozesses zu lösen - von der Datenaufbereitung über den Aufbau bis hin zur Bereitstellung von Modellen.
Machine arning Design Patterns: Rozwiązania wspólnych problemów w przygotowywaniu danych, budowaniu modeli i MLOp (pierwsze wydanie) Autorzy: Trzech inżynierów Google (nazwiska wstrzymane) Wydawca: O'Reilly Media Data publikacji: Kwiecień 2019 Strony: 368 strony Gatunek: Technologia, sztuczna inteligencja, uczenie maszynowe Podsumowanie: Machine arning Design Patterns: Rozwiązania wspólnych problemów w zakresie przygotowywania danych, budowania modeli i MLOp to kompleksowy przewodnik, który zapewnia praktyczne rozwiązania powtarzających się problemów, z którymi borykają się naukowcy zajmujący się uczeniem danych i praktycy uczenia maszynowego. Książka zawiera najlepsze praktyki i sprawdzone techniki pomagające w rozwiązywaniu wspólnych problemów w całym procesie ML - od przygotowania danych po budowanie i wdrażanie modeli.
רצח בגן עדן מותחן על ידי דילן קספר בעתיד הקרוב, העולם הפך לסיוט דיסטופי. שינויי האקלים הרסו את כדור הארץ, מלחמות משאבים השמידו אומות שלמות, והמפרץ בין האבות לבין בעלי-אין גדל כל-כך, עד שנראה שאי-אפשר להתגבר עליו. אבל בתוך כל התוהו ובוהו הזה, יש תקווה. בדמותו של דילן קספר, חוקר מבריק וחסר פחד שהקדיש את חייו לפתרון המקרים הקשים והמסוכנים ביותר. כאשר שרשרת רציחות מטלטלת את העיר האחרונה שנותרה על פני כדור הארץ, גן עדן, דילן מוקצה למצוא את הרוצח לפני שיהיה מאוחר מדי. בעוד דילן מתעמק במקרה, הוא מגלה שהרציחות הן לא רק מעשי אלימות אקראיים, אלא אסטרטגיה מתוכננת בקפידה כדי לערער את השלום השברירי שקיים בגן העדן. בעזרת הצוות שלו, כולל עוזרו הנאמן, ג 'ינקס, בינה מלאכותית מבריקה שתוכנתה לעזור לו, דילן חייב לנווט בנוף הבוגדני של התפתחות הטכנולוגיה כדי לחשוף את האמת מאחורי הרציחות.''
Makine Öğrenimi Tasarım Kalıpları: Veri Hazırlama, Model Oluşturma ve MLOps'ta Ortak Sorunlara Çözümler (İlk Baskı) Yazarlar: Üç Google Mühendisi (isimler saklı) Yayıncı: O'Reilly Media Yayın Tarihi: Nisan 2019 Sayfalar: 368 sayfalar Tür: Teknoloji, Yapay Zeka, Makine Öğrenimi Özet: Makine Öğrenimi Tasarım Kalıpları: Veri Hazırlama, Model Oluşturma ve MLOps'daki Yaygın Sorunlara Çözümler, veri bilimcileri ve makine öğrenimi uygulayıcılarının karşılaştığı yinelenen sorunlara pratik çözümler sunan kapsamlı bir kılavuzdur. Kitap, veri hazırlama, model oluşturma ve dağıtımdan ML süreci boyunca sık karşılaşılan sorunları çözmeye yardımcı olacak en iyi uygulamaları ve kanıtlanmış teknikleri içerir.
أنماط تصميم التعلم الآلي: حلول للمشاكل الشائعة في إعداد البيانات، وبناء النماذج، ومؤلفو MLOps (الطبعة الأولى): ثلاثة مهندسي Google (تم حجب الأسماء) الناشر: تاريخ نشر O'Reilly Media: أبريل 2019 الصفحات: 368 صفحة النوع: التكنولوجيا والذكاء الاصطناعي وملخص التعلم الآلي: أنماط تصميم التعلم الآلي: حلول للمشاكل الشائعة في إعداد البيانات، وبناء النماذج، و MLOps هو دليل شامل يوفر حلولاً عملية للمشاكل المتكررة التي يواجهها علماء البيانات وممارسو التعلم الآلي. يحتوي الكتاب على أفضل الممارسات والتقنيات المثبتة للمساعدة في حل المشكلات المشتركة طوال عملية ML - من إعداد البيانات إلى بناء النموذج ونشره.
머신 러닝 디자인 패턴: 데이터 준비, 모델 빌딩 및 MLops (First Edition) 저자의 일반적인 문제에 대한 솔루션: 3 명의 Google 엔지니어 (이름 보류) 게시자: O'Reilly Media Publication 날짜: 2019 년 4 월 페이지: 368 페이지 장르: 기술, 인공 지능, 기계 학습 요약: 머신 러닝 디자인 패턴: 데이터 준비, 모델 빌딩 및 MLops의 일반적인 문제에 대한 솔루션은 데이터 과학자 및 머신 러닝 실무자가 직면 한 반복되는 문제에 대한 실용적인 솔루션을 제공하는 포괄적 인 안내서입니다. 이 책에는 데이터 준비에서 모델 구축 및 배포에 이르기까지 ML 프로세스 전반에 걸쳐 일반적인 문제를 해결하는 데 도움이되는 모범 사례와 입증
Machine arning Design Patterns:データ準備、モデルビルディング、MLOps (First Edition)の一般的な問題に対する解決策: 3人のGoogleエンジニア(名前は保持)パブリッシャー:O'Reilly Media Publication Date: 4月2019ページ:368ページジャンル:テクノロジー、人工知能、機械学習の概要: 機械学習デザインパターン:データ準備、モデルビルディング、MLOpsにおける共通の問題の解決策は、データサイエンティストや機械学習プラクティショナーが直面する繰り返しの問題に対する実践的な解決策を提供する包括的なガイドです。この本には、データの準備からモデルの構築と展開まで、MLプロセス全体で共通の問題を解決するためのベストプラクティスと実証済みのテクニックが含まれています。
機器學習設計模板:解決數據準備、模型構建和MLOps中常見的問題(第一版)作者: 三名Google工程師(未列出姓名)出版商:O'Reilly Media發布日期: 20194月頁面:368頁體裁:技術,人工智能,機器學習摘要: 機器學習設計模板:解決數據準備、模型構建和MLOps中常見的問題是一個全面的指南,它為數據分析專家和機器學習實踐者面臨的重復性問題提供了切實可行的解決方案。該書匯集了最佳實踐和行之有效的方法,以幫助解決整個ML流程中的常見問題-從數據準備到模型構建和部署。

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