Platform and Model Design for Responsible AI

Develop the skills to design responsible AI projects, including model privacy, fairness, and risk assessment methodologies for scalable distributed systems. Explainability features and sustainable model practices are also covered.

Key Features

• Learn risk assessment for machine learning frameworks for use 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, used for everything from recruiting to deciding who will get a loan. With such widespread use of AI in the decision-making process, it is essential that we build an explainable, responsible, and trustworthy AI enabled systems.

Using this book, you will 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.

Complete with step-by-step explanations of essential concepts, practical examples, and self-assessment questions, you will begin to master designing ethical models for traditional and deep learning ML models as well as deploying them in a sustainable production setup.

You'll learn how to set up data pipelines, validate datasets, and set up component microservices in a secured, private fashion 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 the book, you will know how the best practices comply with laws regarding data privacy and ethics, plus the techniques needed for data anonymization. You will be able to develop models with explainability features, store them in feature stores and handle uncertainty in the model predictions.

What you will learn

• Understand the threats and risks involved in machine learning models
• Discover varying levels of risk mitigation strategies and risk tiering tools
• Apply traditional and deep learning optimization techniques efficiently
• Build auditable, interpretable ML models and feature stores.
• Develop models for different clouds including AWS, Azure and GCP
• Incorporate privacy and fairness in ML models from design to deployment
• Industry wide use-cases centered around Finance, Retail, and Healthcare
• Organizational strategies for leadership across domain use-cases

Who This Book Is For

This book is primarily intended for those who have previous machine learning experience and would like to know about the risks and leakages of ML models and frameworks, and how 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. Emergence of risk-averse methodologies and frameworks
3. Nationwide laws and policies surrounding Trustworthy AI
4. Privacy management in Big Data Pipelines and Model Design
5. Machine Learning Pipeline, Model Evaluation and Handling Uncertainty
6. Hyperparameter Tuning, MLOPS, and AutoML options
7. Fairness in Data Collection
8. Fairness in Model Optimization
9. Model Explainability
10. Ethics and Model Governance
11. Model Adaptability under Ethics
12. Building Scalable Enterprise-grade Trustworthy AI platforms
13. Sustainable Feature Stores and Model calibration
14. Industry-wide Ethical AI Use-cases