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  • Chubb
  • Philadelphia, PA
  • 00:12 - 4h behind
  • LinkedIn in/ani717

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ANI717/README.md

👋 Hi, I'm Animesh Bala Ani

I’m a Backend API Developer specializing in deploying Machine Learning and AI models at scale. My work focuses on building robust, production-grade services that bridge the gap between intelligent models and real-world applications.

🔍 Interests

I'm passionate about:

  • MLOps

  • Natural Language Processing (NLP)

  • Generative AI

  • Computer Vision

  • Machine Learning & Deep Learning

🧠 Expertise

  • Designing and developing models in NLP, Computer Vision, Machine Learning, and Deep Learning.

  • Building, testing, and deploying RESTful APIs and middleware around ML/AI models using frameworks like FastAPI and Docker/Kubernetes.

  • Developing robotic software and control systems with ROS (Robot Operating System).

📬 How to Reach Me

Connect with me on LinkedIn
📧 Mail me at animesh.ani@live.com

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  1. XGBoost-MLOps-Pipeline Public

    An end-to-end machine learning pipeline using XGBoost trained on the sklearn Breast Cancer dataset. This project demonstrates a full production workflow.

    Jupyter Notebook

  2. ANI717-Robotics Public

    Contains ROS2 packages to run robot car to collect annotated camera images while controlled by Gamepad. Also contains packages to run a robot car autonomously with a trained neural network.

    Roff 5 2

  3. Self-Driving-Computer-Vision-Repository Public

    Computer Vision & Deep Learning Repository for Autonomous Driving of Jetson Nano/Raspberry Pi controlled Miniature Robot Cars. The Deep Learning models are trained to mimic robot car driving contro…

    1 2

  4. Localization-with-UNet-Semantic-Segmentation Public

    Car Localization with Semantic Segmentation, using UNet. Dice Score: 0.89, Pixel to Pixel Accuracy: 99.07%.

    Python 1 1

  5. Colorization-with-Pix2Pix-GAN Public

    Colorization of Image with Generative Adversarial Network (Pix2Pix).

    Python 2

  6. Pneumonia_Detection_Effecientnet_B7 Public

    Pneumonia Detection in Chest X-ray Image with EfficientNet-B7. Accuracy = 87.98%, Precision = 100%, Recall = 83.87%, F1 Score = 91.23.

    Python