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Author: sfarrukhm

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+# 🚀 Sentiment Analysis MLOps Pipeline
+
+An **end-to-end MLOps project** demonstrating model deployment, observability, and performance optimization using **FastAPI**, **AWS S3**, and **GitHub Actions**.
+
+This repository shows how to take an NLP model from **fine-tuning to scalable inference** — with dynamic quantization and CI/CD automation for real-world readiness.
+
+---
+
+## 🧠 Project Overview
+
+This project serves a fine-tuned **DistilBERT sentiment classifier** through a production-grade API.
+It includes model loading from **AWS S3**, optional **quantized inference**, structured **logging**, **load testing**, and **CI/CD automation**.
+
+Quantization reduced average latency by **≈60%**, proving the practical value of lightweight model optimization.
+
+| Mode                 | Avg Latency (ms) | P95 Latency (ms) | Improvement   |
+| -------------------- | ---------------- | ---------------- | ------------- |
+| Without Quantization | 3274.15          | 5912.65          | —             |
+| With Quantization    | **1302.57**      | **2581.50**      | ✅ ~60% faster |
+
+---
+
+## 🧩 Tech Stack
+
+| Category         | Tools Used                                 |
+| ---------------- | ------------------------------------------ |
+| **Modeling**     | Hugging Face Transformers (DistilBERT)     |
+| **Serving**      | FastAPI, Uvicorn                           |
+| **Deployment**   | AWS EC2, S3                                |
+| **Automation**   | GitHub Actions (CI/CD)                     |
+| **Monitoring**   | Custom structured logging (`logs/app.log`) |
+| **Testing**      | Pytest                                     |
+| **Load Testing** | Async load simulator with aiohttp          |
+| **Optimization** | PyTorch Dynamic Quantization               |
+
+---
+
+## ⚙️ Key Features
+
+* 🔁 **Automated model fetch from S3** during startup
+* ⚙️ **Dynamic quantization toggle** for faster CPU inference
+* 📈 **Structured request logging** (latency, client IP, text length, sentiment)
+* 🧪 **Pytest-based CI pipeline** for stability
+* 🌐 **FastAPI endpoint** for real-time predictions
+* 📊 **Load simulator** to measure performance under concurrent requests
+
+---
+
+## 🧰 API Usage
+
+### **Health Check**
+
+```bash
+GET /ping
+```
+
+✅ Response:
+
+```json
+{"status": 200, "quantized": false}
+```
+
+### **Predict Endpoint**
+
+```bash
+POST /predict
+```
+
+**Body:**
+
+```json
+{
+  "text": "The movie was absolutely fantastic!",
+  "quantize": true
+}
+```
+
+✅ Response:
+
+```json
+{
+  "sentiment": "positive",
+  "latency_ms": "1320.45",
+  "quantized": true
+}
+```
+
+---
+
+## 📦 Project Structure
+
+```
+sentiment-mlops/
+├── .github/
+│   └── workflows/
+│       ├── ci.yml              # Continuous Integration: pytest, lint checks
+│       └── cd.yml              # Continuous Deployment: deploy to EC2
+│
+├── app/
+│   ├── serve.py                # FastAPI app for serving predictions
+│   └── utils.py                # Model loading, quantization, and inference logic
+│
+├── logs/
+│   ├── app.log                 # Application logs
+│   ├── latency-stats.txt       # Performance summary
+│   └── simulator.log           # Load test results
+│
+├── scripts/
+│   ├── load_simulator.py       # Simulates concurrent requests for stress testing
+│   └── analyze_simulator_logs.py # Parses and visualizes latency results
+│
+├── deploy.sh                   # Shell script for EC2 deployment
+├── Makefile                    # Unified dev commands (test, run, deploy)
+├── requirements.txt             # Project dependencies
+├── test_setup.py                # Basic API and health-check tests
+└── README.md                    # Documentation (you’re reading it!)
+
+```
+
+---
+
+## 🔄 CI/CD Pipeline
+
+GitHub Actions automates:
+
+* ✅ Environment setup (Python + dependencies)
+* ✅ Linting & testing via `pytest`
+* ✅ Failure alerts on PRs
+* ✅ (Optional) Deployment to EC2 after passing tests
+
+This ensures your API is **always tested before merging**, just like real MLOps production pipelines.
+
+---
+
+## 📊 Load Testing
+
+Run async load simulation to test stability under concurrent requests:
+
+```bash
+python scripts/load_simulator.py
+```
+
+Results are logged to `logs/simulator.log` and visualized with a latency-over-time plot.
+
+---
+
+## ⚡ Quantization Impact
+
+| Metric               | Without Quantization | With Quantization |
+| -------------------- | -------------------- | ----------------- |
+| **Min Latency (ms)** | 331.52               | 291.97            |
+| **Avg Latency (ms)** | 3274.15              | **1302.57**       |
+| **P95 Latency (ms)** | 5912.65              | **2581.50**       |
+| **Max Latency (ms)** | 9143.29              | **4830.67**       |
+
+👉 Demonstrates how **PyTorch dynamic quantization** reduces model size and speeds up inference — essential for CPU-based deployments.
+
+---
+
+## 🧪 Run Tests
+
+```bash
+make test
+```
+
+or
+
+```bash
+PYTHONPATH=. pytest -v
+```
+
+---
+
+## ☁️ Deployment
+
+The app is designed for **AWS EC2 deployment**.
+Model files are fetched from **S3** on first run and cached locally.
+
+```bash
+uvicorn app.serve:app --host 0.0.0.0 --port 8000
+```
+
+---
+
+## 🧠 MLOps Pitch
+
+This project demonstrates a **production-ready NLP deployment pipeline** with performance optimization and automation at its core.
+By integrating **AWS**, **FastAPI**, and **GitHub Actions**, it showcases how MLOps turns research models into **reliable, scalable services** — cutting latency by **60%** through smart model quantization.
+
+---
+
+## 👨‍💻 Author
+
+**M. Farrukh Mehmood**  
+📫 [[email protected]](mailto:[email protected])  
+
+🔗 [LinkedIn](https://www.linkedin.com/in/sfarrukhm) | [GitHub](https://github.com/sfarrukhm)