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.

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Spis treści

Platform and Model Design for Responsible AI. Design and build resilient, private, fair, and transparent machine learning models eBook -- spis treści

• 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