Build Python skills through real data problems similar to what ML engineers and data scientists face in production systems. Each exercise simulates a practical engineering scenario, not a toy coding task.

End-to-end MLOps project for vehicle insurance prediction with ZenML, featuring data drift detection and automatic model retraining.

Herramienta CLI que detecta drift entre datasets de producción y entrenamiento usando KL Divergence. Genera reportes automáticos en Markdown vía GitHub Actions. Pensada para auditoría de calidad de datos en sistemas distribuidos

End-to-end MLOps pipeline for loan default prediction with automated retraining, data drift detection, model monitoring, MLflow tracking, Airflow orchestration, FastAPI deployment, and CI/CD automation.

AI Security Metrics - Accuracy Monitoring and Data Drift Detection

End-to-end ML monitoring system with data drift detection, performance monitoring and streamlit dashboard

Python framework for unsupervised multidimensional discourse drift detection

Semantic drift detection for NLP using Sentence Transformers. Detect ML model degradation in production with embedding analysis.

This project builds a production-grade ML pipeline to classify Near-Earth Objects (NEOs) as hazardous or non-hazardous. It automates data ingestion, preprocessing, model training, monitoring, and drift detection using GitHub Actions, PostgreSQL, MLflow, DAGsHub, and Grafana.