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Preface
Part 1: Risk Assessment Machine Learning Frameworks in a Global Landscape
1 Risks and Attacks on ML Models
2 The Emergence of Risk-Averse Methodologies and Frameworks
3 Regulations and Policies Surrounding Trustworthy AI
Part 2: Building Blocks and Patterns for a Next-Generation AI Ecosystem
4 Privacy Management in Big Data and Model Design Pipelines
5 ML Pipeline, Model Evaluation, and Handling Uncertainty
6 Hyperparameter Tuning, MLOps, and AutoML
Part 3: Design Patterns for Model Optimization and Life Cycle Management
7 Fairness Notions and Fair Data Generation
8 Fairness in Model Optimization
9 Model Explainability
10 Ethics and Model Governance
Part 4: Implementing an Organization Strategy, Best Practices, and Use Cases
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
Index
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Sharmistha Chatterjee is an evangelist in the field of machine learning (ML) and cloud applications, currently working in the BFSI industry at the Commonwealth Bank of Australia in the data and analytics space. She has worked in Fortune 500 companies, as well as in early-stage start-ups. She became an advocate for responsible AI during her tenure at Publicis Sapient, where she led the digital transformation of clients across industry verticals. She is an international speaker at various tech conferences and a 2X Google Devel oper Expert in ML and Google Cloud. She has won multiple awards and has been listed in 40 under 40 data scientists by Analytics India Magazine (AIM) and 21 tech trailblazers in 2021 by Google. She has been involved in responsible AI initiatives led by Nasscom and as part of their DeepTech Club.
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