A ⚡️ Lightning.ai ⚡️ component for train and test data drift detection

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

By using Singapore's Ministry of Health's data and the process of Exploratory Data Analysis, it's evidently shown that Singapore's Covid-19 high vaccination rate has helped to see drops in hospital admissions.

An end-to-end ML monitoring pipeline using Evidently AI. Tracks data drift, model performance, and sentiment analysis on student feedback using synthetic datasets. Includes full metrics evaluation, text descriptors, and auto-generated HTML reports.

The Network Traffic Prediction project classifies network traffic for security analysis using FastAPI, handling `Benign`, `DDoS-ACK`, and `DDoS-PSH-ACK` types. It includes privacy-conscious data preprocessing, a user-friendly frontend, and Evidently AI monitoring for data drift and performance tracking.

🚀 ABSA-Drift: Production ML Drift Detection Pipeline | Final project for ML-Ops Zoomcamp 2025: Aspect-Based Sentiment Analysis drift monitoring system for TikTok comment data using AWS serverless architecture, XGBoost, MLflow, and Evidently AI. Features real-time drift detection, automated alerting, and comprehensive monitoring dashboards