Add AI-Assisted Predictive Model Monitoring Guide

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Production ML models require continuous monitoring for performance degradation and drift. This guide demonstrates how to leverage LLMs to interpret metrics, detect drift patterns, and build intelligent alerting systems that reduce noise while surfacing actionable insights.

Changes

New Documentation: docs/predictive-monitoring.md

• Metric Interpretation with LLM Prompts: Ready-to-use templates for explaining Gini coefficients, calibration plots, and PSI/CSI to stakeholders
• Drift Detection Architecture: AI-powered system distinguishing data drift from concept drift with statistical tests (KS, Wasserstein) + LLM interpretation
• Production Monitoring Framework: Complete Python implementation with classes for performance tracking, drift monitoring, and intelligent alerting
• Best Practices: Privacy-preserving patterns, cost controls, alert fatigue reduction

Updated: README.md

• Added guide to content categories table

Example Usage

# Interpret Gini drop with contextual LLM analysis
monitor = ModelMonitor(config)
monitor.run_daily_monitoring(
    current_data=production_df,
    performance_metrics=PerformanceMetrics(gini=0.58, auc=0.78, ...),
    baseline_data=train_df
)

# Automatically generates:
# - Drift detection (PSI, feature-level analysis)
# - LLM-summarized daily reports
# - Intelligent alerts with root cause analysis

The monitoring framework filters alerts through LLM analysis to distinguish signal from noise, reducing alert fatigue while maintaining model health visibility.

Original prompt

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This section details on the original issue you should resolve

<issue_title>AI-Assisted Predictive Model Monitoring & Performance Analysis Guide</issue_title>
<issue_description>Create a comprehensive step-by-step guide for data scientists on leveraging AI to interpret and monitor predictive model performance (e.g., credit, fraud, marketing models).

The guide should cover:

1. Metric Interpretation: Using LLMs to explain and analyze critical metrics such as AUC/Gini, Calibration plots, and PSI/CSI (Population/Characteristic Stability Index).
2. Drift Detection: Strategies for using AI to identify and characterize concept and data drift.
3. Monitoring Systems: Best practices for building AI-enhanced performance monitoring systems and intelligent alerting frameworks that reduce noise and highlight actionable insights.</issue_description>

<agent_instructions>Draft a detailed guide in docs/predictive-monitoring.md. Structuring it as a practical walkthrough for data scientists. Include specific prompts they can use to ask an LLM to interpret a Gini coefficient or a PSI table. Also, outline a Python-based architecture for an automated drift alert system that uses an LLM to summarize daily performance reports.</agent_instructions>

Comments on the Issue (you are @copilot in this section)

• Fixes AI-Assisted Predictive Model Monitoring & Performance Analysis Guide #22

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