End-to-End MLOps Project
Built a complete production-ready Machine Learning pipeline to predict student performance based on demographic, academic, and socio-economic factors. The project focuses on implementing an end-to-end ML workflow — from data ingestion and preprocessing to model training, evaluation, deployment, and monitoring.
Category:
AI & ML
My Role:
AIML Engineer
The objective was to design a scalable MLOps architecture that automates the machine learning lifecycle and enables reliable model deployment in a real-world environment.
The goal was to build a predictive system that can estimate student performance based on various input factors such as:
Gender
Ethnicity
Parental education level
Lunch program
Test preparation course
Reading, Writing, and Mathematics scores
Approach in this project
Built an end-to-end MLOps pipeline by following a structured machine learning workflow:
Collected and analyzed student performance data through EDA.
Developed automated data ingestion and preprocessing pipelines.
Applied feature engineering, encoding, and scaling techniques.
Trained and evaluated multiple ML models to identify the best performer.
Serialized the trained model and transformation pipeline.
Deployed the model using Flask API for real-time predictions.
Designed the project with a modular architecture to support scalability and future enhancements.
Developed a complete end-to-end ML pipeline covering the entire machine learning lifecycle, including data processing, model training, evaluation, and deployment. Built a reusable and scalable MLOps architecture designed for real-world machine learning applications. Successfully deployed a prediction API to generate student performance predictions while improving model reliability through systematic evaluation and automated preprocessing workflows. This project provided practical experience in designing and implementing production-ready ML systems.
The major challenges involved designing a modular pipeline structure where each ML component remained independent, maintainable, and reusable. Ensuring consistency between training and inference workflows required careful management of preprocessing steps.
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