Sharmistha Chatterjee – författare

Craft ethical AI projects with privacy, fairness, and risk assessment features for scalable and distributed systems while maintaining explainability and sustainabilityPurchase of the print or Kindle book includes a free PDF eBookKey FeaturesLearn risk assessment for machine learning frameworks in a global landscapeDiscover patterns for next-generation AI ecosystems for successful product designMake explainable predictions for privacy and fairness-enabled ML trainingBook DescriptionAI 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 learnUnderstand the threats and risks involved in ML modelsDiscover varying levels of risk mitigation strategies and risk tiering toolsApply traditional and deep learning optimization techniques efficientlyBuild auditable and interpretable ML models and feature storesUnderstand the concept of uncertainty and explore model explainability toolsDevelop models for different clouds including AWS, Azure, and GCPExplore ML orchestration tools such as Kubeflow and Vertex AIIncorporate privacy and fairness in ML models from design to deploymentWho this book is forThis 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.

Craft ethical AI projects with privacy, fairness, and risk assessment features for scalable and distributed systems while maintaining explainability and sustainabilityPurchase of the print or Kindle book includes a free PDF eBook

Key Features

Learn risk assessment for machine learning frameworks in a global landscapeDiscover patterns for next-generation AI ecosystems for successful product designMake 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 modelsDiscover varying levels of risk mitigation strategies and risk tiering toolsApply traditional and deep learning optimization techniques efficientlyBuild auditable and interpretable ML models and feature storesUnderstand the concept of uncertainty and explore model explainability toolsDevelop models for different clouds including AWS, Azure, and GCPExplore ML orchestration tools such as Kubeflow and Vertex AIIncorporate 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.

This book is your comprehensive guide into the dynamic intersection of artificial intelligence (AI) and youth mental health. It aims to bridge the gap between cutting-edge AI technology and its transformative potential in addressing youth mental health challenges. The book's content is structured into three key parts, each focusing on different facets of AI applications in youth mental health. The first part provides a comprehensive background on the current state of youth mental health, analyzing the prevalence of mental health issues and identifying the unique challenges faced by the digital generation. In the second part, we explore the foundational principles of AI and its potential for revolutionizing mental health care, including natural language processing, machine learning, and predictive analytics. In this section, you will find in-depth case studies and real-world applications that showcase how AI-driven interventions have already transformed mental health care for youth across diverse contexts. Finally, the third part delves into ethical considerations, fairness, privacy concerns, and the responsible integration of AI in youth mental health care to design long-term sustainable solutions. This book offers a unique and holistic perspective, making it an indispensable resource for anyone passionate about leveraging AI for the betterment of youth mental health. Through this book, you will gain the knowledge and tools needed to design and implement effective AI-driven solutions that have the potential to transform the mental health landscape for the benefit of future generations. What You Will LearnUnderstand the current state of youth mental health, exploring the prevalence of mental health issues among the digital generationUnderstand natural language processing, machine learning, and predictive analyticsKnow how AI interventions are already transforming mental health care for youth in diverse contextsBe aware of fairness, privacy concerns, and the responsible integration of AI in youth mental health careGet familiar with the role of GenAI in the mental health domain and how AI agents can be a game-changer Who This Book Is ForTo equip academics and researchers in the AI, computer science, and digital mental health domain as well as AI application developers with a deeper understanding of how AI-powered innovations can enhance the well-being of youth; and innovation managers and policymakers who are interested in exploring the AI use cases