Skip to content
#

drift-detection

Here are 1,116 public repositories matching this topic...

Data stream analytics: Implement online learning methods to address concept drift and model drift in data streams using the River library. Code for the paper entitled "PWPAE: An Ensemble Framework for Concept Drift Adaptation in IoT Data Streams" published in IEEE GlobeCom 2021.

  • Updated Jun 5, 2023
  • Jupyter Notebook

Persistent session memory for AI coding agents — local-first, with on-device inference, associative recall, and drift detection. Works with Claude Code, Cursor, and Codex.

  • Updated Sep 23, 2026
  • TypeScript

CapyMOA does efficient machine learning for data streams in Python. CapyMOA is a toolbox of methods and evaluators for: classification, regression, clustering, anomaly detection, semi-supervised learning, online continual learning, and drift detection for data streams.

  • Updated Sep 22, 2026
  • Jupyter Notebook
buildomator

Structured plan/execute/verify coding workflow for Claude Code: atomic commits, MCP-backed state, ~92% lower per-turn token overhead, native convention + drift-detection safeguards, and an ASD-STE100 (Simplified Technical English) anti-slop docs gate. A Claude Code-native evolution of GSD and VibeDrift.

  • Updated Sep 20, 2026
  • TypeScript

Add this topic to your repo

To associate your repository with the drift-detection topic, visit your repo's landing page and select "manage topics."

Learn more