User documentation for KServe.

Toolkit for evaluating and monitoring AI models in clinical settings

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

Algorithms for outlier, adversarial and drift detection

Identify kubernetes resources which are not managed by GitOps

Utility to detect stale resources in Kubernetes clusters based on local manifests

Helm plugin that identifies the configuration that has drifted from the Helm chart

Automated Terraform cloud and enterprise drift detection

⚓ Eurybia monitors model drift over time and securizes model deployment with data validation

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

This project adopts a modular Python architecture within an MLOps framework to enhance subscription renewal predictions, utilizing FastAPI and MongoDB with AWS integration (S3, ECR, EC2). Docker ensures seamless deployment, and GitHub Actions automate the CI/CD workflows. Evidently AI monitors drift to guarantee predictive accuracy and reliability.

"1 config, 1 command from Jupyter Notebook to serve Millions of users", Full-stack On-Premises MLOps system for Computer Vision from Data versioning to Model monitoring and drift detection.

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

Zero Trust AI 360

Monitor the stability of a Pandas or Spark dataframe ⚙︎

Uses GitHub Actions to periodically conduct an infrastructure drift detection check.

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.

Online and batch-based concept and data drift detection algorithms to monitor and maintain ML performance.

Dynamic Ensemble Diversification