Algorithms for outlier, adversarial and drift detection
Monitor the stability of a Pandas or Spark dataframe ⚙︎
Drift Detection for your PyTorch Models
Frouros: an open-source Python library for drift detection in machine learning systems.
⚓ Eurybia monitors model drift over time and securizes model deployment with data validation
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.
Forge Orchestrator: Multi-AI task orchestration. File locking, knowledge capture, drift detection. Rust.
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.
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.
The Tornado 🌪️ framework, designed and implemented for adaptive online learning and data stream mining in Python.
User documentation for KServe.
Identify kubernetes resources which are not managed by GitOps
Learn how to monitor ML systems to identify and mitigate sources of drift before model performance decay.
A fast, comprehensive tool for mapping and inventorying AWS resources across 150+ services and all regions.
Free, self-hosted M365 configuration drift monitoring. Baseline your tenant, detect changes at the property level, and restore in one click. For MSSPs and admins.
A toolkit for evaluating and monitoring AI models in clinical settings
Helm plugin that identifies the configuration that has drifted from the Helm chart
CinnaMon is a Python library which offers a number of tools to detect, explain, and correct data drift in a machine learning system
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