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end-to-end-pipeline

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Developed an end-to-end ML system on Azure to predict loan defaults, leveraging advanced data preprocessing, feature engineering, and machine learning models to optimize accuracy. This project includes a comprehensive suite of tools and techniques for robust financial risk assessment, deployed to enhance decision-making for high-risk exposures.

  • Updated Apr 21, 2024
  • Jupyter Notebook

This project is an end-to-end MLOps pipeline for a network security system that detects phishing and malicious activities using machine learning. It automates data ingestion, preprocessing, model training, and deployment while leveraging AWS S3 for model storage and GitHub Actions for CI/CD. The system includes realtime monitoring & a web interface

  • Updated Apr 15, 2025
  • Python

This tutorial walks through the process of building an end-to-end service. It covers setting up a conda environment, creating functions, exposing it through an API, and running the API locally, how to dockerize the service using Dockerfile and docker-compose, and finally, how to access and interact with the containerized service.

  • Updated Oct 10, 2024
  • Python

Full-stack MLOps pipeline for predicting colorectal cancer patient survival using Gradient Boosting, Kubeflow Pipelines, MLflow, and Flask. Designed for hospitals, researchers, and real-world healthcare applications.

  • Updated Apr 9, 2025
  • Jupyter Notebook

Successfully established a machine learning model which can accurately predict the expected life duration of a human being based on several demographic features such as alcohol consumption per capita, average BMI of entire population, etc.

  • Updated Oct 11, 2023
  • Jupyter Notebook

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