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Book Overview & Buying
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Table Of Contents
Platform and Model Design for Responsible AI
By :
Platform and Model Design for Responsible AI
By:
Overview of this book
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.
Table of Contents (21 chapters)
Preface
Part 1: Risk Assessment Machine Learning Frameworks in a Global Landscape
Free Chapter
Chapter 1: Risks and Attacks on ML Models
Chapter 2: The Emergence of Risk-Averse Methodologies and Frameworks
Chapter 3: Regulations and Policies Surrounding Trustworthy AI
Part 2: Building Blocks and Patterns for a Next-Generation AI Ecosystem
Chapter 4: Privacy Management in Big Data and Model Design Pipelines
Chapter 5: ML Pipeline, Model Evaluation, and Handling Uncertainty
Chapter 6: Hyperparameter Tuning, MLOps, and AutoML
Part 3: Design Patterns for Model Optimization and Life Cycle Management
Chapter 7: Fairness Notions and Fair Data Generation
Chapter 8: Fairness in Model Optimization
Chapter 9: Model Explainability
Chapter 10: Ethics and Model Governance
Part 4: Implementing an Organization Strategy, Best Practices, and Use Cases
Chapter 11: The Ethics of Model Adaptability
Chapter 12: Building Sustainable Enterprise-Grade AI Platforms
Chapter 13: Sustainable Model Life Cycle Management, Feature Stores, and Model Calibration
Chapter 14: Industry-Wide Use Cases
Index
