POSTER_CONTENT.md

🫁 PneumoOps: Continuous MLOps Pipeline for 14-Class Thoracic Diagnostics

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🎯 Project Task

An advanced, continuous ML/DL Ops pipeline designed for multi-label chest X-ray classification. The system containerizes models via Docker and deploys them to Hugging Face Spaces. It features live A/B testing between standard and optimized (ONNX) deployments, paired with robust data drift monitoring for 14 thoracic diseases, bridging the gap between lab research and continuous production.

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🛠️ Focus on Deployment & Maintenance

Traditional clinical models natively degrade over time as scanner hardware and patient demographics shift. PneumoOps addresses this "Lab-to-Production" friction through proactive maintenance:

• Real-time Latency Tracking: Enables active comparison of model serving speeds, ensuring responsiveness on varying clinical hardware profiles in production environments.
• Data Drift Detection: Continuously compares incoming clinical image distributions against the original training baseline. Discovering out-of-distribution uploads instantly triggers a Drift Detected alert—providing the automated, programmatic trigger needed for model retraining cycles in true enterprise MLOps.

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💻 Libraries / APIs

The pipeline is powered by a modern, full-stack open source MLOps ecosystem:

• PyTorch (Model Training & Baseline Backend)
• ONNX Runtime (Model Optimization & Accelerated Inference)
• FastAPI (High-performance API Routing & Drift Computation)
• Gradio (Interactive Maintenance Console & Dashboard)
• Docker (Environment Standardization & Containerization)
• Hugging Face Hub/Spaces (Cloud Hosting & Artifact Registry)

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🏗️ Deployment Architecture

A streamlined, automated flow from data to deployment:

1. Train → Train Baseline MobileNetV3-small & Export to ONNX.
2. Push to HF Hub → Push version-controlled weights, training metrics, and code.
3. Dockerized FastAPI Router → Routes live traffic dynamically (50/50) for rapid A/B testing and computes drift statistics on the fly.
4. Gradio UI Dashboard → Empowers clinical data teams with an interactive maintenance dashboard showing top-3 predictions, model-arm comparisons, and system data-drift alerts.

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📊 Datasets and Models

• Dataset: ChestMNIST — A highly efficient, 14-class multi-label dataset. Supports rapid CI/CD iteration loops while capturing complex thoracic conditions (e.g., Cardiomegaly, Pneumonia, Consolidation, Effusion, Mass).
• Model A (Baseline): MobileNetV3-small in standard PyTorch format, tailored for 14 output nodes.
• Model B (Optimized): MobileNetV3-small converted explicitly to ONNX architecture for faster execution speeds and reduced client hardware strain.