ASSIGNMENT_TARGET.md

PneumoOps Assignment Target

Title

PneumoOps: Continuous MLOps Pipeline with A/B Testing & Drift Monitoring for Multi-Class Thoracic Diagnostics

Project Task

Build an advanced, continuous ML/DL Ops pipeline for 14-class chest X-ray classification. The system will deploy containerized models via Docker on Hugging Face Spaces, featuring live A/B testing between a standard and an optimized (ONNX) model. With a strict focus on real-world maintenance, the backend includes real-time inference latency tracking and a data drift monitoring system that triggers automated alerts for model retraining when production data shifts.

Idea Explanation in Detail

This project upgrades a multi-label classification task into a complete, production-ready MLOps system, addressing real-world "Lab-to-Production" challenges and ongoing maintenance. In a real clinical setting, models degrade over time as scanner hardware changes or patient demographics shift. Instead of deploying a single static model, we upload two versions to the Hugging Face Hub: a baseline PyTorch model and an ONNX-optimized model. The Dockerized FastAPI backend performs A/B testing by randomly routing user uploads to either model, allowing us to compare real-world inference latency and performance. Crucially for deployment maintenance, the backend includes a Data Drift Monitor that compares the incoming image's statistical distribution against the training dataset. If an out-of-distribution image is uploaded, the system flags it as "Drift Detected," simulating the exact trigger required for automated retraining cycles in enterprise MLOps.

Datasets and Models to be used

• Dataset: ChestMNIST (14-class multi-label dataset containing Atelectasis, Cardiomegaly, Consolidation, Edema, Effusion, Emphysema, Fibrosis, Hernia, Infiltration, Mass, Nodule, Pleural Thickening, Pneumonia, Pneumothorax). It is lightweight enough for rapid CI/CD iterations without wasting hours on training.
• Model A (Baseline): MobileNetV3-small (Standard PyTorch format adapted for 14 output nodes).
• Model B (Optimized): MobileNetV3-small (Converted to ONNX format for faster client-level inference).

Task 1: The Gradio Web UI

The UI needs to have an image upload component for a chest X-ray. When the user clicks submit, the app should send the image via a requests.post call to a local FastAPI backend (assume the URL is http://127.0.0.1:8000/predict).

The UI needs to display the following output fields clearly:

• Predictions: A bar chart or clean list showing the top 3 highest-probability conditions out of the 14 thoracic disease classes, e.g., Cardiomegaly, Effusion, Mass
• Model Used: This will show either 'Baseline PyTorch' or 'Optimized ONNX' for our A/B testing
• Inference Latency: (ms)
• Data Drift Alert: Will show 'Normal' or 'Drift Detected' with a warning color to simulate production maintenance alerts

Please make the UI look clean, professional, and add a nice title and description at the top explaining that this is an MLOps A/B Testing Pipeline focused on real-world deployment and reliability.

Task 2: The Poster Text

Draft the text content for an academic project poster. Title: PneumoOps: Continuous MLOps Pipeline for 14-Class Thoracic Diagnostics. Generate professional, concise text for the following sections:

• Project Task
• Focus on Deployment & Maintenance
• Libraries / APIs
• Deployment Architecture
• Datasets and Models Keep the text punchy and readable for a visual poster presentation.