Applied Machine Learning Explainability Techniques: Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more

Leverage top XAI frameworks to explain your machine learning models with ease and discover best practices and guidelines to build scalable explainable ML systems

Key Features:

• Explore various explainability methods for designing robust and scalable explainable ML systems
• Use XAI frameworks such as LIME and SHAP to make ML models explainable to solve practical problems
• Design user-centric explainable ML systems using guidelines provided for industrial applications

Book Description:

Explainable AI (XAI) is an emerging field that brings artificial intelligence (AI) closer to non-technical end users. XAI makes machine learning (ML) models transparent and trustworthy along with promoting AI adoption for industrial and research use cases.

Applied Machine Learning Explainability Techniques comes with a unique blend of industrial and academic research perspectives to help you acquire practical XAI skills. You'll begin by gaining a conceptual understanding of XAI and why it's so important in AI. Next, you'll get the practical experience needed to utilize XAI in AI/ML problem-solving processes using state-of-the-art methods and frameworks. Finally, you'll get the essential guidelines needed to take your XAI journey to the next level and bridge the existing gaps between AI and end users.

By the end of this ML book, you'll be equipped with best practices in the AI/ML life cycle and will be able to implement XAI methods and approaches using Python to solve industrial problems, successfully addressing key pain points encountered.

What You Will Learn:

• Explore various explanation methods and their evaluation criteria
• Learn model explanation methods for structured and unstructured data
• Apply data-centric XAI for practical problem-solving
• Hands-on exposure to LIME, SHAP, TCAV, DALEX, ALIBI, DiCE, and others
• Discover industrial best practices for explainable ML systems
• Use user-centric XAI to bring AI closer to non-technical end users
• Address open challenges in XAI using the recommended guidelines

Who this book is for:

This book is for scientists, researchers, engineers, architects, and managers who are actively engaged in machine learning and related fields. Anyone who is interested in problem-solving using AI will benefit from this book. Foundational knowledge of Python, ML, DL, and data science is recommended. AI/ML experts working with data science, ML, DL, and AI will be able to put their knowledge to work with this practical guide. This book is ideal for you if you're a data and AI scientist, AI/ML engineer, AI/ML product manager, AI product owner, AI/ML researcher, and UX and HCI researcher.

利用頂尖的可解釋人工智慧（XAI）框架輕鬆解釋您的機器學習模型，並發現構建可擴展可解釋機器學習系統的最佳實踐和指南。

主要特點：
- 探索各種解釋性方法，設計強大且可擴展的可解釋機器學習系統
- 使用LIME和SHAP等XAI框架，使機器學習模型可解釋，解決實際問題
- 使用提供的工業應用指南，設計以用戶為中心的可解釋機器學習系統

書籍描述：
可解釋人工智慧（XAI）是一個新興領域，將人工智慧（AI）帶給非技術終端用戶。XAI使機器學習（ML）模型透明且可信，並促進AI在工業和研究用例中的應用。

《應用機器學習可解釋性技術》結合了工業和學術研究的觀點，幫助您獲得實用的XAI技能。您將首先對XAI有概念性的理解，以及為什麼在AI中如此重要。接下來，您將獲得實際經驗，使用最先進的方法和框架在AI/ML問題解決過程中應用XAI。最後，您將獲得必要的指南，將您的XAI之旅提升到更高的水平，彌合AI和終端用戶之間的差距。

通過閱讀本書，您將掌握AI/ML生命周期的最佳實踐，並能夠使用Python實施XAI方法和方法來解決工業問題，成功應對遇到的關鍵痛點。

學到什麼：
- 探索各種解釋方法及其評估標準
- 學習結構化和非結構化數據的模型解釋方法
- 應用以數據為中心的XAI進行實際問題解決
- 實踐LIME、SHAP、TCAV、DALEX、ALIBI、DiCE等方法
- 發現可解釋機器學習系統的工業最佳實踐
- 使用以用戶為中心的XAI將AI帶給非技術終端用戶
- 使用推薦的指南解決XAI中的開放性挑戰

適合閱讀對象：
本書適合從事機器學習和相關領域的科學家、研究人員、工程師、架構師和管理人員。任何對使用AI進行問題解決感興趣的人都會從本書中受益。建議具備Python、ML、DL和數據科學的基礎知識。從事數據科學、ML、DL和AI的AI/ML專家將能夠通過這本實用指南將他們的知識應用於實際工作。本書對於數據和AI科學家、AI/ML工程師、AI/ML產品經理、AI產品負責人、AI/ML研究人員以及UX和HCI研究人員非常適合。