Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and Mlops (Paperback)
暫譯: 機器學習設計模式：數據準備、模型構建及 MLOps 的常見挑戰解決方案 (平裝本)

Lakshmanan, Valliappa, Robinson, Sara, Munn, Michael

Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and Mlops (Paperback)

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商品描述

The design patterns in this book capture best practices and solutions to recurring problems in machine learning. Authors Valliappa Lakshmanan, Sara Robinson, and Michael Munn catalog proven methods to help engineers tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice.

The authors, three Google Cloud engineers, describe 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation.

You'll learn how to:

  • Identify and mitigate common challenges when training, evaluating, and deploying ML models
  • Represent data for different ML model types, including embeddings, feature crosses, and more
  • Choose the right model type for specific problems
  • Build a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuning
  • Deploy scalable ML systems that you can retrain and update to reflect new data
  • Interpret model predictions for stakeholders and ensure models are treating users fairly

商品描述(中文翻譯)

本書中的設計模式捕捉了機器學習中的最佳實踐和解決重複問題的方案。作者 Valliappa Lakshmanan、Sara Robinson 和 Michael Munn 編纂了經過驗證的方法，幫助工程師解決整個機器學習過程中的常見問題。這些設計模式將數百位專家的經驗編碼為簡單易懂的建議。

這三位 Google Cloud 工程師描述了 30 種模式，涵蓋數據和問題表示、操作化、可重複性、可再現性、靈活性、可解釋性和公平性。每個模式都包括問題的描述、多種潛在解決方案，以及針對特定情況選擇最佳技術的建議。

您將學會如何：

- 識別和減輕在訓練、評估和部署機器學習模型時的常見挑戰
- 為不同的機器學習模型類型表示數據，包括嵌入、特徵交叉等
- 為特定問題選擇合適的模型類型
- 構建一個穩健的訓練循環，使用檢查點、分佈策略和超參數調整
- 部署可擴展的機器學習系統，並能夠重新訓練和更新以反映新數據
- 解釋模型預測給利益相關者，並確保模型對用戶的對待是公平的

作者簡介

Valliappa (Lak) Lakshmanan is Global Head for Data Analytics and AI Solutions on Google Cloud. His team builds software solutions for business problems using Google Cloud's data analytics and machine learning products. He founded Google's Advanced Solutions Lab ML Immersion program. Before Google, Lak was a Director of Data Science at Climate Corporation and a Research Scientist at NOAA.

Sara Robinson is a Developer Advocate on Google's Cloud Platform team, focusing on machine learning. She inspires developers and data scientists to integrate ML into their applications through demos, online content, and events. Sara has a bachelor's degree from Brandeis University. Before Google, she was a Developer Advocate on the Firebase team.

Michael Munn is an ML Solutions Engineer at Google where he works with customers of Google Cloud on helping them design, implement, and deploy machine learning models. He also teaches an ML Immersion Program at the Advanced Solutions Lab. Michael has a PhD in mathematics from the City University of New York. Before joining Google, he worked as a research professor.

作者簡介(中文翻譯)

Valliappa (Lak) Lakshmanan 是 Google Cloud 數據分析和人工智慧解決方案的全球負責人。他的團隊利用 Google Cloud 的數據分析和機器學習產品為商業問題構建軟體解決方案。他創立了 Google 的高級解決方案實驗室機器學習沉浸計畫。在加入 Google 之前，Lak 曾擔任 Climate Corporation 的數據科學總監以及 NOAA 的研究科學家。

Sara Robinson 是 Google Cloud Platform 團隊的開發者倡導者，專注於機器學習。她通過示範、線上內容和活動，激勵開發者和數據科學家將機器學習整合到他們的應用程式中。Sara 擁有布蘭代斯大學的學士學位。在加入 Google 之前，她曾是 Firebase 團隊的開發者倡導者。

Michael Munn 是 Google 的機器學習解決方案工程師，他與 Google Cloud 的客戶合作，幫助他們設計、實施和部署機器學習模型。他還在高級解決方案實驗室教授機器學習沉浸計畫。Michael 擁有紐約市立大學的數學博士學位。在加入 Google 之前，他曾擔任研究教授。

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