Metadata-Version: 2.4
Name: modelshift
Version: 0.1.0
Summary: A lightweight machine learning drift monitoring and alerting engine.
Author: Krishna
Author-email: ryomensukuna2530@gmail.com
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: pandas
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: requests
Dynamic: author
Dynamic: author-email
Dynamic: classifier
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# 🚦 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

```text
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

