Practical Mlops: Operationalizing Machine Learning Models

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

將上述文字翻譯成繁體中文如下：

「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，旨在以可靠且自動化的方式解決這個問題。這本富有洞察力的指南將帶領您了解 MLOps 是什麼（以及它與 DevOps 的區別），並向您展示如何實踐它以使您的機器學習模型運作起來。」

「現有和有志於成為機器學習工程師的人，或者對數據科學和 Python 有所了解的人，將在 MLOps 工具和方法（以及 AutoML、監控和日誌記錄）方面建立基礎，然後學習如何在 AWS、Microsoft Azure 和 Google Cloud 上實施它們。您越快交付一個有效的機器學習系統，您就能越快專注於您試圖解決的業務問題。這本書將讓您提前起步。」

「您將發現如何：」

「- 將 DevOps 最佳實踐應用於機器學習」
「- 構建生產機器學習系統並維護它們」
「- 監控、儀表化、負載測試和運營化機器學習系統」
「- 選擇適合特定機器學習任務的 MLOps 工具」
「- 在各種平台和設備上運行機器學習模型，包括手機和專用硬件」

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.

Noah Gift 是 Pragmatic A.I. Labs 的創辦人。他在 MSDS、Northwestern、Duke MIDS 研究生資料科學計畫、UC Berkeley 研究生資料科學計畫、UC Davis Graduate School of Management MSBA 計畫、UNC Charlotte 資料科學計畫以及 University of Tennessee（作為 Tennessee Digital Jobs Factory 的一部分）擔任講師。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服務。作為前任 CTO、個人貢獻者和顧問，他在電影、遊戲和 SaaS 等多個行業擁有超過 20 年的經驗，並成功推出了多個帶來收入的產品。

Alfredo Deza 是一位熱情的軟體工程師、演講者、作家和前奧運選手，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演講，內容涵蓋軟體開發、個人發展和職業運動。Alfredo 已經撰寫了多本關於 DevOps 和 Python 的書籍，並在課程、書籍和演講中持續分享他對強韌基礎架構、測試和穩健開發實踐的知識。