# Telco churn Lakehouse and MLOps

> Reproducible Databricks Medallion Lakehouse and MLOps lifecycle with execution evidence.

- Category: Big Data · MLOps
- Año / Year: 2026

## Context

Build a reproducible data and ML lifecycle for predicting customer churn while making the synthetic nature of the dataset explicit.

## Highlights

- End-to-end co-implementation confirmed by Alonso on 2026-09-14
- MLflow experiment, three-task ML job and simulation run associated with Alonso
- Shared work across the Medallion pipeline, model lifecycle and daily validation

## Results

- Training rows: 16,316,445. Fuente: README.md#resultados-evidenciados
- Distinct customers: 2,042,162. Fuente: README.md#resultados-evidenciados
- Model features: 33. Fuente: README.md#resultados-evidenciados
- Training window: 2023-07 → 2024-12. Fuente: README.md#resultados-evidenciados

## Technology stack

Databricks · PySpark · Delta Lake · Auto Loader · Databricks Asset Bundles · MLflow · Unity Catalog · Lakehouse Monitoring

## Verifiable links

- Portfolio page: https://alonsomarcosm.github.io/en/projects/telco-churn-mlops-databricks/
- GitHub repository: https://github.com/AlonsoMarcosM/databricks-telco-churn-lakehouse
- technical_docs: https://alonsomarcosm.github.io/databricks-telco-churn-lakehouse/