Research-first machine learning experiment tracker for comparing model metrics, scalar curves, artifacts, and experiment lineage.

Run Observation & Artifact Registration

This project is a PySpark-based customer churn prediction pipeline that covers the full machine learning lifecycle, from raw data ingestion and preprocessing through model training, evaluation, and streaming inference. It uses MLflow for experiment tracking and artifact management, with a production-focused setup for reproducibility and monitoring.

This project implements a telco churn prediction pipeline with local Apache Airflow orchestration, native Apache Kafka streaming, and PySpark-based model training and inference.

Local-first governance and operational accountability system for AI work, with lifecycle structure, artifact tracking, and durable memory.