Managing Machine Learning Projects: From Design to Deployment
暫譯: 管理機器學習專案：從設計到部署
Thompson, Simon
- 出版商: Manning
- 出版日期: 2023-08-22
- 定價: $1,950
- 售價: 9.5 折 $1,853
- 貴賓價: 9.0 折 $1,755
- 語言: 英文
- 頁數: 246
- 裝訂: Quality Paper - also called trade paper
- ISBN: 163343902X
- ISBN-13: 9781633439023
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相關分類:
Machine Learning
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相關翻譯:
機器學習項目成功交付 (簡中版)
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商品描述
Guide machine learning projects from design to production with the techniques in this unique project management guide. No ML skills required! In Managing Machine Learning Projects you'll learn essential machine learning project management techniques, including:
Managing Machine Learning Projects is an end-to-end guide for delivering machine learning applications on time and under budget. It lays out tools, approaches, and processes designed to handle the unique challenges of machine learning project management. You'll follow an in-depth case study through a series of sprints and see how to put each technique into practice. The book's strong consideration to data privacy, and community impact ensure your projects are ethical, compliant with global legislation, and avoid being exposed to failure from bias and other issues. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Ferrying machine learning projects to production often feels like navigating uncharted waters. From accounting for large data resources to tracking and evaluating multiple models, machine learning technology has radically different requirements than traditional software. Never fear! This book lays out the unique practices you'll need to ensure your projects succeed. About the Book Managing Machine Learning Projects is an amazing source of battle-tested techniques for effective delivery of real-life machine learning solutions. The book is laid out across a series of sprints that take you from a project proposal all the way to deployment into production. You'll learn how to plan essential infrastructure, coordinate experimentation, protect sensitive data, and reliably measure model performance. Many ML projects fail to create real value--read this book to make sure your project is a success. What's Inside
2 Pre-project: From opportunity to requirements
3 Pre-project: From requirements to proposal
4 Getting started
5 Diving into the problem
6 EDA, ethics, and baseline evaluations
7 Making useful models with ML
8 Testing and selection
9 Sprint 3: system building and production
10 Post project (sprint O)
- Understanding an ML project's requirements
- Setting up the infrastructure for the project and resourcing a team
- Working with clients and other stakeholders
- Dealing with data resources and bringing them into the project for use
- Handling the lifecycle of models in the project
- Managing the application of ML algorithms
- Evaluating the performance of algorithms and models
- Making decisions about which models to adopt for delivery
- Taking models through development and testing
- Integrating models with production systems to create effective applications
- Steps and behaviors for managing the ethical implications of ML technology
Managing Machine Learning Projects is an end-to-end guide for delivering machine learning applications on time and under budget. It lays out tools, approaches, and processes designed to handle the unique challenges of machine learning project management. You'll follow an in-depth case study through a series of sprints and see how to put each technique into practice. The book's strong consideration to data privacy, and community impact ensure your projects are ethical, compliant with global legislation, and avoid being exposed to failure from bias and other issues. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Ferrying machine learning projects to production often feels like navigating uncharted waters. From accounting for large data resources to tracking and evaluating multiple models, machine learning technology has radically different requirements than traditional software. Never fear! This book lays out the unique practices you'll need to ensure your projects succeed. About the Book Managing Machine Learning Projects is an amazing source of battle-tested techniques for effective delivery of real-life machine learning solutions. The book is laid out across a series of sprints that take you from a project proposal all the way to deployment into production. You'll learn how to plan essential infrastructure, coordinate experimentation, protect sensitive data, and reliably measure model performance. Many ML projects fail to create real value--read this book to make sure your project is a success. What's Inside
- Set up infrastructure and resource a team
- Bring data resources into a project
- Accurately estimate time and effort
- Evaluate which models to adopt for delivery
- Integrate models into effective applications
2 Pre-project: From opportunity to requirements
3 Pre-project: From requirements to proposal
4 Getting started
5 Diving into the problem
6 EDA, ethics, and baseline evaluations
7 Making useful models with ML
8 Testing and selection
9 Sprint 3: system building and production
10 Post project (sprint O)
商品描述(中文翻譯)
使用本獨特的專案管理指南中的技術，從設計到生產引導機器學習專案。無需機器學習技能！
在管理機器學習專案中，您將學習到基本的機器學習專案管理技術，包括：- 了解機器學習專案的需求
- 為專案設置基礎設施並配置團隊資源
- 與客戶及其他利益相關者合作
- 處理數據資源並將其引入專案中使用
- 管理專案中模型的生命週期
- 管理機器學習算法的應用
- 評估算法和模型的性能
- 決定採用哪些模型進行交付
- 將模型帶入開發和測試階段
- 將模型與生產系統整合以創建有效的應用程式
- 管理機器學習技術的倫理影響的步驟和行為
管理機器學習專案是一本從頭到尾的指南，旨在按時且在預算內交付機器學習應用程式。它列出了應對機器學習專案管理獨特挑戰所需的工具、方法和流程。您將通過一系列的衝刺跟隨深入的案例研究，並學習如何將每種技術付諸實踐。本書對數據隱私和社區影響的強烈考量，確保您的專案是倫理的，符合全球法規，並避免因偏見和其他問題而導致失敗。 購買印刷版書籍可獲得Manning Publications提供的免費PDF、Kindle和ePub格式電子書。 關於技術 將機器學習專案推向生產，常常感覺像是在航行未知水域。從考慮大量數據資源到跟蹤和評估多個模型，機器學習技術的需求與傳統軟體截然不同。別擔心！本書列出了確保您的專案成功所需的獨特實踐。 關於本書 管理機器學習專案是有效交付現實機器學習解決方案的經驗豐富的技術來源。本書分為一系列衝刺，帶您從專案提案一路到生產部署。您將學習如何規劃基本基礎設施、協調實驗、保護敏感數據以及可靠地測量模型性能。許多機器學習專案未能創造真正的價值——閱讀本書以確保您的專案成功。 內容概覽
- 設置基礎設施並配置團隊資源
- 將數據資源引入專案
- 準確估算時間和精力
- 評估採用哪些模型進行交付
- 將模型整合到有效的應用程式中
2 專案前期：從機會到需求
3 專案前期：從需求到提案
4 開始
5 深入問題
6 EDA、倫理和基線評估
7 使用機器學習製作有用的模型
8 測試和選擇
9 第三衝刺：系統建設和生產
10 專案後期（衝刺O）
作者簡介
Simon Thompson has spent 25 years developing AI systems. He led the AI research program at BT Labs in the UK, where he helped pioneer Big Data technology in the company and managed an applied research practice for nearly a decade. Simon now works delivering Machine Learning systems for financial services companies in the City of London as the Head of Data Science at GFT Technologies.
作者簡介(中文翻譯)
西蒙·湯普森 在人工智慧系統的開發上已經有 25 年的經驗。他曾在英國的 BT Labs 領導人工智慧研究計畫，幫助公司開創了大數據技術，並管理了一個應用研究實踐近十年。西蒙目前擔任 GFT Technologies 的數據科學部門主管，為倫敦金融服務公司提供機器學習系統。
