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

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

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.

These are my notes of the Udacity Nanodegree Machine Learning DevOps Engineer.

Simulation, testing and comparison of state of the art Unsupervised Concept Drift Detectors used in a batch Machine Learning scenario.

資料科學的日常研究議題

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).

Learn how to handle model drift and perform test-based model monitoring

ModelPulse helps maintain model reliability and performance by providing early warning signals for these issues, allowing teams to address them before they impact users significantly.

Production-style ML monitoring template on the Wine Quality (red) dataset: Evidently (data/target/prediction drift, data quality) + adversarial validation, PSI/JS effect sizes, SHAP/PDP, slice analysis, and an Alert Policy with actions

"Past performance of machine learning model is no guarantee of future results." We call it "model drift" or "model decay". This repository will introduce various methods for detecting model drift.

This repository lists one of my projects and findings as part of my Machine Learning DevOps Engineer Nanodegree.

An ML monitoring framework, applied to an attrition risk assessment system.

Plataforma de referência em MLOps e LLMOps para Telecom, usando Google Cloud (Vertex AI, BigQuery, Cloud Run) para construir um modelo de churn em batch e um assistente LLM com RAG, CI/CD e monitoramento de ponta a ponta.

Time‑aware NBA forecasting pipeline (R² 0.94 points) with rolling CV, leakage guards, and automated retraining; includes backtesting reports and model card.

Prompt engineering framework + evaluation harness for LLM workflows (classification, summarization, extraction).