Platform and Model Design for Responsible AI: Design and build resilient, private, fair, and transparent machine learning models (Paperback or Softback)

Kapoor, Amita

ISBN 10: 1803237074 / ISBN 13: 9781803237077

Edité par Packt Publishing 4/28/2023, 2023

A propos de cet article

Platform and Model Design for Responsible AI: Design and build resilient, private, fair, and transparent machine learning models.

N° de réf. du vendeur BBS-9781803237077

Détails bibliographiques

• Titre: Platform and Model Design for Responsible AI: Design and build resilient, private, fair, and transparent machine learning models (Paperback or Softback)
• Éditeur: Packt Publishing 4/28/2023
• Date de parution: 2023
• Langue: anglais
• ISBN à 10 chiffres: 1803237074
• ISBN à 13 chiffres: 9781803237077
• Reliure: Paperback or Softback
• État de l'article: New
• Poids: 1,93 livre
• Type de livre: Book

A propos de ce titre

Synopsis

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.

Table of Contents

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

À propos de l?auteur

Amita Kapoor is an accomplished AI consultant and educator, with over 25 years of experience. She has received international recognition for her work, including the DAAD fellowship and the Intel Developer Mesh AI Innovator Award. She is a highly respected scholar in her field, with over 100 research papers and several best-selling books on deep learning and AI. After teaching for 25 years at the University of Delhi, Amita took early retirement and turned her focus to democratizing AI education. She currently serves as a member of the Board of Directors for the non-profit Neuromatch Academy, fostering greater accessibility to knowledge and resources in the field. Following her retirement, Amita also founded NePeur, a company that provides data analytics and AI consultancy services. In addition, she shares her expertise with a global audience by teaching online classes on data science and AI at the University of Oxford.

Sharmistha Chatterjee is an evangelist in the field of machine learning (ML) and cloud applications, currently working in the BFSI industry at the Commonwealth Bank of Australia in the data and analytics space. She has worked in Fortune 500 companies, as well as in early-stage start-ups. She became an advocate for responsible AI during her tenure at Publicis Sapient, where she led the digital transformation of clients across industry verticals. She is an international speaker at various tech conferences and a 2X Google Developer Expert in ML and Google Cloud. She has won multiple awards and has been listed in 40 under 40 data scientists by Analytics India Magazine (AIM) and 21 tech trailblazers in 2021 by Google. She has been involved in responsible AI initiatives led by Nasscom and as part of their DeepTech Club.