ZAYAN
AHMAD
GHOUS.
I build the infrastructure that takes ML models from experimentation to production designing robust pipelines, automating model deployment, and ensuring models stay reliable at scale.
What I work with
Building ML pipelines, backend APIs, and optimized algorithms.
Data cleaning, visualization, and feature engineering for ML models.
Developing and training machine learning and deep learning models.
Currently learning model deployment, tracking, and scalable pipelines.
Version control, experimentation, and data visualization tools.
Projects
End-to-end machine learning project that predicts house prices using a trained regression model. Includes data preprocessing, feature engineering, FastAPI deployment, and Docker containerization.
Developed a deep learning model using tensorflow. Deployed interactive web app using hugging face spaces. Impletemented real-time image prediction system.
Developed and deployed a real-time ML-based diabetes prediction system with an interactive UI using Gradio on Hugging Face.
Developed a machine learning model to predict the likelihood of heart disease based on patient data, achieving 85% accuracy on a dataset patients.
The Smart Habit Tracker is an interactive application built with Object-Oriented Programming (OOP) principles and Streamlit for visualization. It allows users to track habits, visualize progress, and receive tailored habit recommendations.
A web application for managing student fees and payments, built with Django.
Education
Studying core CS fundamentals with a focus on machine learning, data systems, and software engineering. Actively building MLOps projects alongside coursework.
CGPA 3.66 / 4.0Studied Computer Science, Mathematics, and Physics. Built a strong foundation in programming and analytical thinking.
Completed secondary education with a focus on science subjects.
Experience
• Built end-to-end preprocessing pipelines (missing values, encoding, scaling)
• Worked with 10,000+ samples for data engineering and cleaning
• Developed CNN model for MRI brain tumor detection using TensorFlow/Keras
• Deployed ML apps using FastAPI and Gradio for real-time inference
• Performed EDA, feature engineering, and model evaluation using Python