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jsuryanm/README.md

Hi 👋, I'm Jayasuryan Mutyala

Building End-to-End AI Systems

  • 🔭 I'm currently working on medical image segmentation using UNet and SOTA (SAM-3) models

  • 🌱 I'm currently learning Generative AI, Agentic AI and MLOps

  • 💬 Ask me about Machine Learning, Deep Learning, NLP, Computer Vision

  • 👨‍💻 All of my projects are available at GitHub

  • 📄 Know about my experiences Resume

Connect with me:

jsuryanm jsuryanm

Languages and Tools:

anaconda aws docker fastapi git huggingface jupyter langchain linux matplotlib mlflow numpy opencv pandas python pytorch scikit_learn seaborn streamlit

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  1. foggy-vehicle-detection foggy-vehicle-detection Public

    End-to-end YOLO object detection system covering dataset preparation, training, evaluation, and deployment. Includes FastAPI, Streamlit UI, Docker, and AWS EC2 for production-ready inference.

    Jupyter Notebook

  2. text-summarization-system text-summarization-system Public

    End-to-End text summarization system built with bart-base using HuggingFace Transformers and deployed using FastAPI, Docker, and AWS.

    Jupyter Notebook 1

  3. chest-cancer-net chest-cancer-net Public

    End-to-End Deep Learning system for chest cancer detection from CT scans, using modular data pipelines, PyTorch-based model training, FastAPI inference service, and an interactive Streamlit UI.

    Python

  4. medical-rag-chatbot medical-rag-chatbot Public

    An end‑to‑end medical question‑answering chatbot built using Retrieval‑Augmented Generation (RAG).

    Jupyter Notebook