Automated Tool for Optimized Modelling
MLOps for deploying a Credit Risk model
MLFlow End to End Workshop at Chandigarh University
Demonstrating the use of Prefect to orchestrate the creation of machine learning surrogate models as applied to mechanistic crop models.
An end-to-end MLOps pipeline for a production-grade fraud detection model. This project demonstrates best practices including data versioning (DVC), experiment tracking (MLflow), CI/CD (GitHub Actions), containerization (Docker), deployment on GKE, and advanced model analysis (poisoning attacks, drift, fairness, explainability).
MLflow workshop held at Applied Machine Learning Days 2020 in Lausanne
Streamlit App for Node and Graph Classification and Explainability
This project fine-tunes a MobileNetV2 model for dog breed classification using labeled image data. It emphasizes strong software engineering practices such as version control, modular code, and continuous integration, ensuring a scalable, maintainable machine learning pipeline with experiment tracking.
Guided assets allocation within a portfolio. 📈
Implémentation complète d’un modèle de scoring crédit avec MLflow, optimisation du seuil de probabilité, interprétation SHAP et déploiement d’une API prédictive sur Heroku.
A deployable End-to-End machine learning model to predict loan default risk of new loan request for a financial services platform
Tuberculosis Vision Scan is an AI-driven tool for detecting tuberculosis from chest X-rays using a fine-tuned VGG16 model with 93.5% accuracy. The project integrates MLOps tools like MLflow, DVC, and AWS CI/CD for efficient model tracking and deployment. A Flask web app allows users to upload X-ray images and receive real-time TB detection results
Detect buried land mines based on magnetic field distortions.
Шаблон системы кредитного скоринга
🚀 Build and enhance machine learning systems with practical tools and insights from a Senior Deep Learning Engineer and Researcher.
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