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Articles and notes describing concepts, implementation patterns, and governance considerations.
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AI Personal Model Architecture: Building User-Specific Recommendation Systems
Technical overview of AI Personal Model architecture for building personalized AI systems that learn from individual user behavior while maintaining privacy and explainability.
AI Model Monitoring: Best Practices for Production Systems
Comprehensive guide to monitoring AI models in production, covering performance metrics, drift detection, data quality, and operational monitoring for financial services.
Explainable AI Techniques for Financial Services
Overview of practical explainability techniques for AI models in financial services, including SHAP, LIME, reason codes, and feature importance methods.
AI Credit Scoring Implementation: from prototype to production
Practical guidance on implementing AI credit scoring systems in financial institutions, covering data preparation, model development, integration, and monitoring.
Responsible AI in Lending: controls and artifacts
A factual overview of governance controls and documentation artifacts commonly used for AI-assisted lending workflows.
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