I open sourced a production MLOps pipeline. Here is what it took to get it to PyPI and Hugging Face in one day.

I have been running ML pipelines in production for few years. Tens of millions of predictions a day, real money on the line, no tolerance for guesswork. PulseFlow started as something I built for myself. A reference architecture I kept recreating from scratch at every company because nothing open source matched what production actually demands. Today I packaged it, published it to PyPI, and put a live demo on Hugging Face. Here is what it covers and how to run it in under ten minutes. What PulseFlow is A production-grade MLOps pipeline you can clone and run immediately. Not a tutorial. Not a toy dataset. A real stack. pip install pulseflow-mlops Five components wired together: ETL pipeline: ingestion and preprocessing with Pandas and SQLAlchemy Training pipeline: model training with MLflow experiment tracking Deployment service: FastAPI microservice for real-time inference Orchestration: Apache Airflow DAGs for end-to-end automation Full Docker Compose stack: one command to run everyth