Drift-Lens: an Unsupervised Drift Detection Framework for Deep Learning Classifiers on Unstructured Data

🌊 Online machine learning in Python

A General Toolkit for Online Learning Approaches

Music album popularity prediction classic ML model showcasting MLOps, versioning, feature selection, cross valdiation and concept drift.

Frouros: an open-source Python library for drift detection in machine learning systems.

A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convol…

Algorithms for outlier, adversarial and drift detection

Credit Card Fraud Detection

Implementation/Tutorial of using Automated Machine Learning (AutoML) methods for static/batch and online/continual learning

A curated list of awesome open source tools and commercial products for monitoring data quality, monitoring model performance, and profiling data 🚀

📖These are the concept drift datasets we made, and we open-source the data and corresponding interfaces. Welcome to use them for free if there is a need.

Drift Lens Demo

The official API of DoubleAdapt (KDD'23), an incremental learning framework for online stock trend forecasting, WITHOUT dependencies on the qlib package.

CADM+: Confusion-based Learning Framework With Drift Detection and Adaptation for Real-time Safety Assessment

This is an official PyTorch implementation of the NeurIPS 2023 paper 《OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling》

This repository includes code for the AutoML-based IDS and adversarial attack defense case studies presented in the paper "Enabling AutoML for Zero-Touch Network Security: Use-Case Driven Analysis" published in IEEE Transactions on Network and Service Management.

Concept Drift Detection and Adaptation Methods - Reference Codes and Papers

Balancing Efficiency vs. Effectiveness and Providing Missing Label Robustness in Multi-Label Stream Classification

An online learning method used to address concept drift and model drift. Code for the paper entitled "A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data Streams" published in IEEE Internet of Things Magazine.

MemStream: Memory-Based Streaming Anomaly Detection