Training a machine learning model is only one part of building a usable ML application.

I created Production MLOps Platform to practise connecting model training, evaluation, prediction APIs, and interactive applications.

Architecture

The project uses synthetic customer churn data and explores:

  • Reproducible dataset generation
  • Logistic Regression classification
  • Model evaluation and saved artifacts
  • FastAPI prediction endpoints
  • Streamlit dashboard integration
  • Pytest and Docker

Current limitations

This is an educational prototype, not a production-ready MLOps system.

The repository currently has a source-folder mismatch: Python files must be reorganised into the expected src/ structure before the documented application commands can work correctly.

Roadmap

I plan to verify the complete application, introduce CI/CD, explore MLflow, add model monitoring, and improve API security.

GitHub repository: https://github.com/tasnem-tech/production-mlops-platform

The project is helping me understand the engineering work required beyond model training.