A prototype for cross-system metric streams, derived metrics, and online anomaly detection.
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Updated
Sep 25, 2026 - Python
A prototype for cross-system metric streams, derived metrics, and online anomaly detection.
Python framework for online learning in machine learning with streaming data pipelines, concept drift detection, and prequential evaluation.
Live ML-powered trading dashboard for NinjaTrader 8 — 10 incremental models with distinct trading personalities that learn in real time, compete via Champion/Challenger, and level up based on simulated P&L.
Client-server backend for real-time Activity of Daily Living (ADL) classification. Handles window ingestion, prediction, label requests, and incremental model updates.
A 100% locally-run Host SOC and Intrusion Detection System (IDS) for Ubuntu Linux featuring real-time journalctl streaming, online anomaly detection with River ML, local SQLite persistence, and an interactive Rich CLI dashboard.
Complete system for the classification of Activities of Daily Living (ADL) by collecting inertial data from smartphones and evaluating supervised models (RF, SVM) under a Stream Learning approach, including online architecture for real-time classification.
Prácticas de la asignatura de Aprendizaje Automático a Gran Escala. Predicción de retrasos en vuelos con PySpark (MLP), aprendizaje federado con Flower y aprendizaje continuo en streaming con River.
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