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Sowaiba-01/README.md

Hi there!

I'm Sowaiba, an AI/ML software engineer and designer.

I build Machine Learning, Deep Learning, and LLM-powered systems, focused on shipping models that are reliable, explainable, and production-ready. Most of my work lives at the intersection of computer vision, NLP, and healthcare AI.

Lately I've been building production-grade medical imaging models, autonomous coding agents, and tooling that keeps ML systems healthy in the real world. I care about the unglamorous parts like uncertainty quantification, model drift, and explainability, the things that decide whether a model can actually be trusted.

Outside of engineering, I design on Canva, write about what I learn, and love exploring whatever's new in AI.

Let's connect!

Dev.to LinkedIn

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  1. ThoraxNet ThoraxNet Public

    Production-grade medical AI framework using BioMedCLIP (ViT-B/16) for multi-label chest X-ray pathology detection. Features Monte Carlo Dropout for uncertainty quantification, per-class threshold c…

    Python 2 1

  2. Darwin Darwin Public

    A self-evolving multi-agent AI system that autonomously writes, sandboxes, evaluates, and improves its own agents with an LLM router, event-driven brain, and a 3D web console.

    Python 2

  3. DeepGuard DeepGuard Public

    A deepfake detection platform using fine-tuned EfficientNet-B4. Features GradCAM explainability to show exactly where a video was manipulated, alongside an InsightFace pipeline.

    TypeScript 2

  4. ModelSentinel ModelSentinel Public

    Open-source Python toolkit that checks whether a trained ML model is still healthy: metrics, data drift, calibration, data quality, and a weighted health score.

    Python 3

  5. AgroVision-Net AgroVision-Net Public

    Deep learning system for plant disease classification across 38 categories using EfficientNetB0 fine-tuning on the PlantVillage dataset. Features a Gradio web interface, confidence thresholding, an…

    Python 7 1

  6. portfolio portfolio Public

    My portfolio: Next.js site with live ML demos, my deployed chest X-ray and deepfake detection models answering in the browser, plus a hand-written TF-IDF RAG engine.

    JavaScript 2