Platform and Model Design for Responsible AI: Design and build resilient, private, fair, and transparent machine learning models
暫譯: 負責任的人工智慧平台與模型設計:設計與建構具韌性、隱私、公平及透明的機器學習模型
Kapoor, Amita, Chatterjee, Sharmistha
- 出版商: Packt Publishing
- 出版日期: 2023-04-28
- 售價: $2,200
- 貴賓價: 9.5 折 $2,090
- 語言: 英文
- 頁數: 516
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1803237074
- ISBN-13: 9781803237077
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相關分類:
Machine Learning
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商品描述
Craft ethical AI projects with privacy, fairness, and risk assessment features for scalable and distributed systems while maintaining explainability and sustainability
Purchase of the print or Kindle book includes a free PDF eBook
Key Features
- Learn risk assessment for machine learning frameworks in a global landscape
- Discover patterns for next-generation AI ecosystems for successful product design
- Make explainable predictions for privacy and fairness-enabled ML training
Book Description
AI algorithms are ubiquitous and used for tasks, from recruiting to deciding who will get a loan. With such widespread use of AI in the decision-making process, it's necessary to build an explainable, responsible, transparent, and trustworthy AI-enabled system. With Platform and Model Design for Responsible AI, you'll be able to make existing black box models transparent.
You'll be able to identify and eliminate bias in your models, deal with uncertainty arising from both data and model limitations, and provide a responsible AI solution. You'll start by designing ethical models for traditional and deep learning ML models, as well as deploying them in a sustainable production setup. After that, you'll learn how to set up data pipelines, validate datasets, and set up component microservices in a secure and private way in any cloud-agnostic framework. You'll then build a fair and private ML model with proper constraints, tune the hyperparameters, and evaluate the model metrics.
By the end of this book, you'll know the best practices to comply with data privacy and ethics laws, in addition to the techniques needed for data anonymization. You'll be able to develop models with explainability, store them in feature stores, and handle uncertainty in model predictions.
What you will learn
- Understand the threats and risks involved in ML models
- Discover varying levels of risk mitigation strategies and risk tiering tools
- Apply traditional and deep learning optimization techniques efficiently
- Build auditable and interpretable ML models and feature stores
- Understand the concept of uncertainty and explore model explainability tools
- Develop models for different clouds including AWS, Azure, and GCP
- Explore ML orchestration tools such as Kubeflow and Vertex AI
- Incorporate privacy and fairness in ML models from design to deployment
Who this book is for
This book is for experienced machine learning professionals looking to understand the risks and leakages of ML models and frameworks, and learn to develop and use reusable components to reduce effort and cost in setting up and maintaining the AI ecosystem.
商品描述(中文翻譯)
打造具備隱私、公平性和風險評估功能的道德 AI 專案,適用於可擴展和分散式系統,同時保持可解釋性和可持續性
購買印刷版或 Kindle 版書籍可獲得免費 PDF 電子書
主要特點
- 學習全球範圍內機器學習框架的風險評估
- 發現下一代 AI 生態系統的模式,以成功設計產品
- 為具備隱私和公平性功能的機器學習訓練做出可解釋的預測
書籍描述
AI 演算法無處不在,應用於從招聘到決定誰能獲得貸款等任務。隨著 AI 在決策過程中的廣泛使用,建立一個可解釋、負責任、透明且值得信賴的 AI 系統變得至關重要。通過《負責任的 AI 平台與模型設計》,您將能夠使現有的黑箱模型變得透明。
您將能夠識別並消除模型中的偏見,處理來自數據和模型限制的不確定性,並提供負責任的 AI 解決方案。您將從設計傳統和深度學習機器學習模型的道德模型開始,並在可持續的生產環境中部署它們。之後,您將學習如何設置數據管道、驗證數據集,並在任何雲無關的框架中以安全和私密的方式設置組件微服務。接著,您將構建一個公平且私密的機器學習模型,設置適當的約束,調整超參數,並評估模型指標。
在本書結束時,您將了解遵守數據隱私和倫理法律的最佳實踐,以及數據匿名化所需的技術。您將能夠開發具備可解釋性的模型,將其存儲在特徵庫中,並處理模型預測中的不確定性。
您將學到什麼
- 了解機器學習模型中涉及的威脅和風險
- 發現不同層級的風險緩解策略和風險分級工具
- 有效應用傳統和深度學習優化技術
- 構建可審計和可解釋的機器學習模型及特徵庫
- 理解不確定性的概念並探索模型可解釋性工具
- 為不同雲平台(包括 AWS、Azure 和 GCP)開發模型
- 探索機器學習編排工具,如 Kubeflow 和 Vertex AI
- 在機器學習模型的設計到部署過程中融入隱私和公平性
本書適合誰
本書適合有經驗的機器學習專業人士,旨在了解機器學習模型和框架的風險和洩漏,並學習開發和使用可重用組件,以減少設置和維護 AI 生態系統的工作量和成本。
目錄大綱
1. Risks and Attacks on ML Models
2. The Emergence of Risk-Averse Methodologies and Frameworks
3. Regulations and Policies Surrounding Trustworthy AI
4. Privacy Management in Big Data and Model Design Pipelines
5. ML Pipeline, Model Evaluation and Handling Uncertainty
6. Hyperparameter Tuning, MLOPS, and AutoML
7. Fairness Notions and Fain Data Generation
8. Fairness in Model Optimization
9. Model Explainability
10. Ethics and Model Governance
11. The Ethics of Model Adaptability
12. Building Sustainable, Enterprise-Grade AI Platforms
13. Sustainable Model Life Cycle Management, Feature Stores, and Model Calibration
14. Industry-Wide Use-cases
目錄大綱(中文翻譯)
1. Risks and Attacks on ML Models
2. The Emergence of Risk-Averse Methodologies and Frameworks
3. Regulations and Policies Surrounding Trustworthy AI
4. Privacy Management in Big Data and Model Design Pipelines
5. ML Pipeline, Model Evaluation and Handling Uncertainty
6. Hyperparameter Tuning, MLOPS, and AutoML
7. Fairness Notions and Fain Data Generation
8. Fairness in Model Optimization
9. Model Explainability
10. Ethics and Model Governance
11. The Ethics of Model Adaptability
12. Building Sustainable, Enterprise-Grade AI Platforms
13. Sustainable Model Life Cycle Management, Feature Stores, and Model Calibration
14. Industry-Wide Use-cases