Platform and Model Design for Responsible AI
- Paperback: 516 pages
- Publisher: WOW! eBook (April 28, 2023)
- Language: English
- ISBN-10: 1803237074
- ISBN-13: 978-1803237077
Platform and Model Design for Responsible AI: Craft ethical AI projects with privacy, fairness, and risk assessment features for scalable and distributed systems while maintaining explainability and sustainability
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.
- 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 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 like Kubeflow and VertexAI
- Incorporate privacy and fairness in ML models from design to deployment
By the end of this Platform and Model Design for Responsible AI 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.