AI-powered medical imaging system for multi-disease chest X-ray detection,built with EfficientNet deep learning, a FastAPI backend, and an interactive Streamlit dashboard. Deployed on Render for real-time healthcare diagnostics, detecting conditions like Atelectasis, Edema and more.An end-to-end project demonstrating model training,API development.

End-to-end MLOps project for predictive maintenance using engine sensor data. Includes data versioning on Hugging Face, MLflow experiment tracking, CI/CD with GitHub Actions, and Dockerized Streamlit deployment for real-time engine failure classification.

An end-to-end MLOps project demonstrating a modular machine learning pipeline for predicting student performance, featuring a Flask web interface and deployment on AWS.

This project performs Exploratory Data Analysis (EDA) on IPL match-level and ball-by-ball datasets to uncover team and player performance insights across 15+ seasons

Implementación de un ciclo de vida MLOps completo: un clasificador de ML servido con FastAPI, Docker, DVC y CI/CD con GitHub Actions.

An end-to-end MLOps project for text summarization using the HuggingFace Pegasus model. Includes a full training pipeline, evaluation, and a FastAPI for deployment.

This project predicts the quality of wine based on its physicochemical properties such as acidity, sugar, pH, and alcohol content. Using machine learning models, the system learns patterns from historical wine data to classify or estimate wine quality, helping automate and improve decision-making in food and beverage analysis.

Deployed a production-ready customer churn prediction system using Flask on Render, delivering real-time churn probability and risk classification.

An end-to-end Machine Learning project featuring a modular pipeline, configuration-driven workflows, MLflow experiment tracking, DagsHub integration, and a Flask web interface, following industry-standard MLOps practices.