A fully automated ML pipeline for customer churn prediction in telecom, orchestrated with Apache Airflow. Covers data ingestion, validation, feature engineering, model training, deployment, and monitoring with DVC-based versioning for complete reproducibility.

End-to-end MLOps pipeline for a Hotel Reservation Cancellation Prediction using Docker, Jenkins, Kubernetes, MLflow, Kubeflow, Prometheus, and Grafana on GCP.

Built a machine-learning system to predict customer interest in vehicle insurance using demographic and behavioral data. Containerized and deployed the model as a web service with a full CI/CD pipeline.

MLOps pipeline for cloud cost prediction with automated CI/CD on AWS

An end-to-end machine learning project built on the UCI Heart Disease dataset, covering data preprocessing, feature engineering, model training, evaluation, and deployment. The project includes Streamlit app that supports both single-patient and batch predictions, ensuring reproducibility through a well-structured pipeline and saved model artifacts

This project implements the classical LeNet-5 CNN for MNIST digit classification using PyTorch. It covers a complete pipeline from data preprocessing to deployment. The model achieves ~98.8% test accuracy, showing the strong effectiveness of early CNN architectures for image classification.

End-to-end Machine Learning project built with Flask, Docker, and deployed on AWS & Azure.

End-to-End MLOps project — predicts whether a professional will switch careers using MongoDB, Scikit-learn, FastAPI, Docker, AWS S3/ECR/EC2, and GitHub Actions CI/CD.

Built an end-to-end ML pipeline integrating MLflow, DagsHub, and AWS SageMaker with DVC-based data versioning. The project includes modular source code, experiment artifacts, logging, Dockerized deployment, and environment management-covering the full ML lifecycle from development to production.

In this post, I share my complete journey of passing the Microsoft DP-100: Designing and Implementing a Data Science Solution on Azure certification. This guide includes my preparation strategy, best study resources, hands-on practice with Azure Machine Learning, and key exam tips that helped me succeed.

An end-to-end MLOps project for classifying malicious URLs, featuring a full CI/CD pipeline with GitHub Actions, Docker, and deployment to AWS EC2.