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

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反覆出現的問題的最佳實踐和解決方案。作者Valliappa Lakshmanan、Sara Robinson和Michael Munn對幫助工程師解決機器學習過程中的常見問題的成熟方法進行了彙編。這些設計模式將數百位專家的經驗轉化為直接、易於理解的建議。

這三位Google Cloud工程師描述了30種用於數據和問題表示、操作化、可重複性、可重現性、靈活性、可解釋性和公平性的模式。每個模式都包括問題的描述、多種潛在解決方案以及選擇最佳技術的建議。

你將學到如何：
- 在訓練、評估和部署機器學習模型時識別和減輕常見挑戰
- 為不同的機器學習模型類型（包括嵌入、特徵交叉等）表示數據
- 選擇特定問題的合適模型類型
- 構建一個強大的訓練循環，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，可以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，並確保模型對待用戶公平。

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的全球數據分析和人工智能解決方案負責人。他的團隊使用Google Cloud的數據分析和機器學習產品為業務問題構建軟件解決方案。他創辦了Google的高級解決方案實驗室ML沉浸式計劃。在加入Google之前，Lak曾是Climate Corporation的數據科學總監和NOAA的研究科學家。

Sara Robinson是Google Cloud平台團隊的開發者倡導者，專注於機器學習。她通過演示、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序中。Sara擁有布蘭迪斯大學的學士學位。在加入Google之前，她曾是Firebase團隊的開發者倡導者。

Michael Munn是Google的ML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ML沉浸式計劃。Michael擁有紐約市立大學的數學博士學位。在加入Google之前，他曾擔任研究教授的職位。

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