data-drift
There are 48 repositories under data-drift topic.
evidentlyai/evidently
Evidently is an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.
deepchecks/deepchecks
Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.
SeldonIO/alibi-detect
Algorithms for outlier, adversarial and drift detection
NannyML/nannyml
nannyml: post-deployment data science in python
Renumics/awesome-open-data-centric-ai
Curated list of open source tooling for data-centric AI on unstructured data.
IFCA-Advanced-Computing/frouros
Frouros: an open-source Python library for drift detection in machine learning systems.
MAIF/eurybia
⚓ Eurybia monitors model drift over time and securizes model deployment with data validation
evidentlyai/ml_observability_course
Free Open-source ML observability course for data scientists and ML engineers. Learn how to monitor and debug your ML models in production.
awesome-mlops/awesome-ml-monitoring
A curated list of awesome open source tools and commercial products for monitoring data quality, monitoring model performance, and profiling data 🚀
VectorInstitute/cyclops
A toolkit for evaluating and monitoring AI models in clinical settings
radicalbit/radicalbit-ai-monitoring
A comprehensive solution for monitoring your AI models in production
mitre/menelaus
Online and batch-based concept and data drift detection algorithms to monitor and maintain ML performance.
neurdb/neurdb
AI-powered Autonomous Data System
roboflow/roboflow-collect
Passively collect images for computer vision datasets on the edge.
ilias-ant/adversarial-validation
A tiny framework to perform adversarial validation of your training and test data.
VishalKumar-S/Sales_Conversion_Optimization_MLOps_Project
Sales Conversion Optimization MLOps: Boost revenue with AI-powered insights. Features H2O AutoML, ZenML pipelines, Neptune.ai tracking, data validation, drift analysis, CI/CD, Streamlit app, Docker, and GitHub Actions. Includes e-mail alerts, Discord/Slack integration, and SHAP interpretability. Streamline ML workflow and enhance sales performance.
aws-samples/sagemaker-model-monitor-bring-your-own-container
In this repository, we will present techniques to detect covariate drift, and demonstrate how to incorporate your own custom drift detection algorithms and visualizations with SageMaker model monitor.
grecosalvatore/drift-lens
Drift-Lens: an Unsupervised Drift Detection Framework for Deep Learning Classifiers on Unstructured Data
Nachimak28/evidently-data-analysis
A ⚡️ Lightning.ai ⚡️ component for train and test data drift detection
korie-cyber/Fraud-Detection-Model
Fraud detection system using machine learning and deep learning (XGBoost + Autoencoder). Trains on synthetic financial transactions to flag suspicious activity with business-focused evaluation metrics.
rajat116/github-anomaly-project
Real-time anomaly detection system for GitHub activity using Airflow, MLflow, and Terraform
grecosalvatore/DriftLensDemo
Drift Lens Demo
pacificrm/MLOPS-Full-Data-Pipeline
An end-to-end MLOps pipeline for a production-grade fraud detection model. This project demonstrates best practices including data versioning (DVC), experiment tracking (MLflow), CI/CD (GitHub Actions), containerization (Docker), deployment on GKE, and advanced model analysis (poisoning attacks, drift, fairness, explainability).
ReverendBayes/Data-Drift-Check
Tabular Data Drift Detector & Reporter: A CLI tool that connects to any database or CSV, computes statistical drift (KS-test, Jensen-Shannon) between “baseline” vs. “current” data, and emits a Markdown/HTML report with charts.
robinsonkwame/adversarial_labeller
Adversarial labeller is a sklearn compatible instance labelling tool for model selection under data drift.
uncleDecart/data_drift
Data Drift detection using auto encoders
ddrous/morebikes
Predicting the number of bicycles at rental stations.
pyladiesams/model-drift-beginner-dec2022
Learn how to handle model drift and perform test-based model monitoring
vathymut/dsos
Dataset shift with outlier scores
Gianatmaja/WhizML
A reusable codebase for fast data science and machine learning experimentation, integrating various open-source tools to support automatic EDA, ML models experimentation and tracking, model inference, model explainability, bias, and data drift analysis.
grecosalvatore/neurosymbolic-explainable-concept-drift
This work investigates the use of neuro-symbolic rules to explain concept drift in machine learning models.
iQuantC/Kolmogorov_Smirnov_ML_DL_AI
In this project, we illustrate how the Kolmogorov Smirnov (KS) statistical test works, and why it is commonly used in Machine Learning (ML), Deep Learning (DL) and Artificial Intelligence (AI).
JBris/nannyml-test
Testing out NannyML
labrijisaad/AXA-Direct-ML-Apprenticeship
Repository showcasing my Machine Learning Engineering Apprenticeship at AXA-Direct Assurance, contributing to the development and implementation of Machine Learning solutions.
Lithin-7/end2end-salary-ml
A complete end-to-end machine learning pipeline to predict employee salaries (USD) using job-related features. Includes automated EDA, robust preprocessing, ML model training with MLflow, drift detection with Evidently AI, and a Flask-based web app for both single and batch predictions.
vanhai1231/ml-drift-monitoring
Agent AI tự động giám sát drift dữ liệu trong pipeline ML, cảnh báo qua email và Slack.