modelshift 0.1.0

A lightweight machine learning drift monitoring and alerting engine.

🚦 ModelShift-Lite

Label-Free Monitoring for Deployed Machine Learning Models

A lightweight, behavior-centric system to detect silent reliability degradation in deployed machine learning models — without requiring ground-truth labels.

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📌 Why ModelShift-Lite?

Machine learning models rarely fail loudly after deployment.
Instead, they silently degrade as real-world data changes — while true labels are unavailable for continuous evaluation.

ModelShift-Lite addresses this blind spot.

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🧩 Problem Statement

Deployed machine learning models often degrade silently over time due to changing data distributions, while ground-truth labels are unavailable for continuous performance evaluation.

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🎯 Project Objective

Design a label-free, post-deployment monitoring system that tracks:

• Data distribution shifts
• Prediction behavior instability
• Model reliability trends

to provide early warning signals of degradation without modifying the deployed model.

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🚫 What This Project Does Not Do

To maintain clarity of scope, ModelShift-Lite explicitly does not:

• ❌ Retrain models
• ❌ Correct predictions
• ❌ Compute accuracy on production data

It focuses solely on monitoring and interpretability.

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🧠 Core Idea (In Simple Terms)

If we cannot measure correctness, we can still monitor behavior.

ModelShift-Lite observes how a model reacts to changing data and identifies signs of instability before failures become obvious.

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🛠️ Key Components

• Reference Baseline Handling
  Captures normal model behavior from historical or validation data

• Live Inference Monitoring
  Tracks incoming production data and predictions

• Feature Drift Detection
  Identifies changes in input distributions

• Prediction Behavior Analysis
  Monitors confidence, stability, and output distribution shifts

• Model Health Scoring
  Aggregates drift signals into an interpretable reliability indicator

• Visualization Dashboard
  Displays trends, drift severity, and degradation warnings

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Reference Data → → Drift Detection → Health Scoring → Monitoring Dashboard Live Inference →

(Detailed architecture diagrams are provided in /docs)

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💻 Technology Stack

• Language: Python
• Data Processing: NumPy, Pandas
• Statistical Analysis: SciPy
• Visualization: Streamlit, Matplotlib
• Storage: SQLite (local, replaceable)

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📂 Repository Structure

modelshift-lite/
├── modelshift/        # Core monitoring logic
├── dashboard/         # Streamlit visualization app
├── experiments/       # Drift simulation & analysis
├── data/              # Reference & live data
├── docs/              # Architecture and design docs
└── README.md
## 🏗️ High-Level Architecture