Machine Learning Engineering with MLflow, published by Packt

Production-grade end-to-end machine learning pipeline with SQL ingestion, feature engineering, model training, experiment tracking (MLflow), and data versioning (DVC).

system provides: Automated preprocessing & feature engineering Interactive EDA dashboards Multiple machine learning models with experiment tracking via MLflow Scenario simulation for business decision-making

House Prices Predictor System: A machine learning application that predicts residential property prices using Python, featuring thorough data analysis, feature engineering, model training, and MLOps integration with ZenML and MLflow.

A machine learning project to evaluate loan application risks using EDA, feature engineering, and models like Random Forest. Tracks results with MLflow and DagsHub. Deployed with FastAPI (backend) and Streamlit (frontend) for real-time predictions.

Production-style end-to-end machine learning pipeline to predict student dropout risk. Includes batch data ingestion, data validation with Great Expectations, feature engineering with Spark, model training with XGBoost, experiment tracking using MLflow, and model serving via FastAPI.

Machine-Learning-Engineering-with-MLflow book

End-to-end machine learning pipeline with reproducible data ingestion, feature engineering, model training, and MLflow experiment tracking.

Advanced Flight Price Prediction Machine Learning Model with MLflow tracking, automated preprocessing, feature engineering, and multi-model comparison.

Full stack data and machine learning engineering, using data of NHL. With python, we have tackle Data fetching, SQLite, data engineering, ETL, EDA, ML models, model registry, serving, MLFlow, Falsk, Streamlit

Malware Detection System using various machine learning and deep learning models, incorporating data preprocessing, feature engineering, and model evaluation techniques, while tracking experiments with MLflow for performance comparison and reproducibility.

End-to-end Retail Demand Forecasting ML system with modular pipelines, feature engineering, MLflow experiment tracking, Flask API deployment, and Docker containerization for scalable machine learning inference.

A complete end-to-end machine learning pipeline for predicting Airbnb listing prices using AWS S3 for data storage and MLflow for experiment tracking. Includes data cleaning, feature engineering, regression model development, and model registration with MLflow.

End-to-end machine learning pipeline for house price prediction — from data preprocessing and feature engineering to model training, experiment tracking with MLflow, and deployment via FastAPI & Streamlit.

Data Science Portfolio | Machine Learning, MLOps & Analytics. A curated collection of projects showcasing applied machine learning, data engineering, and analytical modeling. Includes end-to-end workflows with Python, MLflow, Docker, and Airflow for reproducible and production-ready solutions.

A machine learning model to predict house prices using features like location and size. Includes EDA, feature engineering, and model deployment with ZenML and MLflow for tracking and scalability.

Machine Learning project to predict house prices using Linear Regression, Random Forest, and LSTM. Includes data preprocessing, feature engineering, model evaluation, and reproducible MLOps tracking with MLflow and ZenML.

A curated collection of end-to-end machine learning projects, featuring modular pipelines, feature engineering, model tracking with MLflow, and clean project structures ready for production or portfolio use

A production-ready machine learning pipeline for predicting customer churn in music streaming services. Built with extensible abstract base classes, comprehensive feature engineering, time-based validation, and MLflow experiment tracking.

A machine learning project to evaluate loan application risks using EDA, feature engineering, and models like Random Forest. Tracks results with MLflow and DagsHub. Deployed with FastAPI (backend) and Streamlit (frontend) for real-time predictions.

End-to-end Sentiment Analysis project with a modular Machine Learning pipeline using DVC for workflow automation and MLflow for experiment tracking. Includes data ingestion, preprocessing, feature engineering, model training, evaluation, and reproducible experimentation.

An end-to-end machine learning pipeline for predicting credit risk in a buy-now-pay-later service. Leverages RFM-based proxy variables, feature engineering, and model training with MLflow, deployed via FastAPI, and automated with CI/CD workflows.

An end-to-end data science project that implements a machine learning pipeline with workflows for data ingestion, validation, feature engineering, model training, and evaluation. The project integrates MLFLOW and Dagshub for tracking and managing experiments.

This project implements a robust MLOps pipeline for predicting hotel booking cancellations. It leverages modern data engineering and machine learning practices, including automated data ingestion, preprocessing with class balancing (SMOTE), feature selection, and model training with hyperparameter optimization and MLflow tracking.

A collection of machine learning experiments and model training notebooks used to build the YouTube Comment Sentiment Analyzer. This repo contains TF-IDF feature engineering, and hyperparameter tuning, all tracked and versioned with MLflow and model registry

EMIPredict AI is a FinTech machine learning platform that predicts EMI eligibility and maximum safe EMI using classification & regression models. Built with 400K financial records, MLflow tracking, advanced feature engineering, and a full Streamlit web app for real-time risk assessment.

This project predicts Airbnb listing prices using a full machine-learning pipeline with data cleaning, feature engineering, and model training. MLflow is used to track experiments, compare models, and manage artifacts. The repository includes visual EDA, preprocessing workflows, and optimized regression models.

A complete end-to-end machine learning project that predicts New York City taxi trip duration using advanced feature engineering, LightGBM, data validation, MLflow tracking, FastAPI inference, and a Streamlit dashboard. Includes CI/CD automation with GitHub Actions and production-ready Docker deployments.