Platform and Model Design for Responsible AI
Platform and Model Design for Responsible AI will help you build ML algorithms that are safe, ethical, robust, auditable, and interpretable. You’ll discover the possible threats and potential causes of unfair ML models. The book addresses issues from a model perspective as well as from an architectural and deployment standpoint, enabling you to comply with regulations and governance standards systematically.
Offered by
Difficulty Level
Intermediate
Completion Time
17h12m
Language
English
About Book
Who Is This Book 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.
Platform and Model Design for Responsible AI
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 17h12m total length
Risks and Attacks on ML Models
The Emergence of Risk-Averse Methodologies and Frameworks
Regulations and Policies Surrounding Trustworthy AI
Privacy Management in Big Data and Model Design Pipelines
ML Pipeline, Model Evaluation and Handling Uncertainty
Hyperparameter Tuning, MLOPS, and AutoML
Fairness Notions and Fain Data Generation
Fairness in Model Optimization
Model Explainability
Ethics and Model Governance
The Ethics of Model Adaptability
Building Sustainable, Enterprise-Grade AI Platforms
Sustainable Model Life Cycle Management, Feature Stores, and Model Calibration
Industry-Wide Use-cases
Related Resources
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