Practical Mlops: Operationalizing Machine Learning Models
暫譯: 實用的 MLOps：機器學習模型的運營化

Gift, Noah, Deza, Alfredo

Practical Mlops: Operationalizing Machine Learning Models
  • 出版商: O'Reilly
  • 出版日期: 2021-10-19
  • 定價: $2,980
  • 售價: 8.8 折 $2,622 (限時優惠至 2025-03-31)
  • 語言: 英文
  • 頁數: 460
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1098103017
  • ISBN-13: 9781098103019
  • 相關分類: Machine Learning
  • 相關翻譯: MLOps權威指南 (簡中版)

    • 立即出貨 (庫存=1)

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商品描述

Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This insightful guide takes you through what MLOps is (and how it differs from DevOps) and shows you how to put it into practice to operationalize your machine learning models.

Current and aspiring machine learning engineers--or anyone familiar with data science and Python--will build a foundation in MLOps tools and methods (along with AutoML and monitoring and logging), then learn how to implement them in AWS, Microsoft Azure, and Google Cloud. The faster you deliver a machine learning system that works, the faster you can focus on the business problems you're trying to crack. This book gives you a head start.

You'll discover how to:

  • Apply DevOps best practices to machine learning
  • Build production machine learning systems and maintain them
  • Monitor, instrument, load-test, and operationalize machine learning systems
  • Choose the correct MLOps tools for a given machine learning task
  • Run machine learning models on a variety of platforms and devices, including mobile phones and specialized hardware

商品描述(中文翻譯)

將模型投入生產是機器學習的基本挑戰。MLOps 提供了一套經過驗證的原則，旨在以可靠和自動化的方式解決這個問題。本指南深入探討了 MLOps 是什麼（以及它與 DevOps 的區別），並展示了如何將其付諸實踐，以使您的機器學習模型運作起來。

目前和未來的機器學習工程師——或任何熟悉數據科學和 Python 的人——將建立 MLOps 工具和方法的基礎（以及 AutoML 和監控與日誌記錄），然後學習如何在 AWS、Microsoft Azure 和 Google Cloud 中實施它們。您交付一個有效的機器學習系統的速度越快，您就能越快專注於您試圖解決的商業問題。本書將為您提供先機。

您將發現如何：

- 將 DevOps 最佳實踐應用於機器學習
- 建立生產級機器學習系統並維護它們
- 監控、儀表化、負載測試並使機器學習系統運作
- 為特定的機器學習任務選擇正確的 MLOps 工具
- 在各種平台和設備上運行機器學習模型，包括手機和專用硬體

作者簡介

Noah Gift is the founder of Pragmatic A.I. Labs. He lectures at MSDS, at Northwestern, Duke MIDS Graduate Data Science Program, the Graduate Data Science program at UC Berkeley, the UC Davis Graduate School of Management MSBA program, UNC Charlotte Data Science Initiative, and University of Tennessee (as part of the Tennessee Digital Jobs Factory). He teaches and designs graduate machine learning, MLOps, AI, and data science courses, and consulting on machine learning and cloud architecture for students and faculty. As a former CTO, individual contributor, and consultant he has over 20 years' experience shipping revenue-generating products in many industries including film, games, and SaaS.

Alfredo Deza is a passionate software engineer, speaker, author, and former Olympic athlete with almost two decades of DevOps and software engineering experience. He currently teaches Machine Learning Engineering and gives worldwide lectures about software development, personal development, and professional sports. Alfredo has written several books about DevOps and Python, and continues to share his knowledge about resilient infrastructure, testing, and robust development practices in courses, books, and presentations.

作者簡介(中文翻譯)

Noah Gift 是 Pragmatic A.I. Labs 的創辦人。他在 MSDS、西北大學、杜克大學 MIDS 研究生資料科學計畫、加州大學伯克利分校研究生資料科學計畫、加州大學戴維斯分校管理研究所 MSBA 計畫、北卡羅來納大學夏洛特分校資料科學計畫以及田納西大學（作為田納西數位工作工廠的一部分）授課。他教授並設計研究生機器學習、MLOps、人工智慧和資料科學課程，並為學生和教職員提供機器學習和雲端架構的諮詢。作為前首席技術官、個別貢獻者和顧問，他在電影、遊戲和 SaaS 等多個行業擁有超過 20 年的經驗，專注於推出創造收入的產品。

Alfredo Deza 是一位充滿熱情的軟體工程師、演講者、作者以及前奧林匹克運動員，擁有近二十年的 DevOps 和軟體工程經驗。他目前教授機器學習工程，並在全球各地進行有關軟體開發、個人發展和專業運動的演講。Alfredo 已經撰寫了幾本有關 DevOps 和 Python 的書籍，並持續在課程、書籍和演講中分享他對彈性基礎設施、測試和穩健開發實踐的知識。

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