continuous-training

Open-Source Machine Learning Platform

🧹 Automated Filtering of Undesirable Web Data to Update LLM Knowledge

Data version control with Makefile and DVC for a regression task to estimate insurance costs for certain individuals.

mini project

🏷️ An AI-driven approach to Label LLM Training Data

The backend of the KOI-system.

The runtime environment for the KOI-System. Train models, run instances, and collect samples.

Fully automated MLOps pipeline with continuous training, drift detection, model versioning, and self-healing deployments using canary releases and real-time monitoring.

A production-grade, end-to-end MLOps platform designed to automate the complete lifecycle of a financial fraud detection system. This repository demonstrates how to transition from a static ML model to a self-healing, automated production system.

End-to-end Financial MLOps pipeline for automated stock forecasting featuring PyTorch LSTM, Apache Airflow orchestration, MLflow tracking, MongoDB Atlas, and FastAPI containerized with Docker and GHCR.

🌱 Manifeste de la Clairveillance : pour des institutions qui mesurent, doutent et apprennent

MLOps platform that keeps a production model healthy: data contracts, MLflow tracking and registry, promotion gates against the champion, drift and performance monitoring that triggers retraining, and SageMaker canary deployments with automatic rollback. Terraform for AWS.

LLM 이 라벨을 달아 재학습하는 개인정보 탐지 NER 모델. 2025년 11월 네이버 클라우드에 배포했고 2026년 1월부터 매일 켜는 일정은 꺼 두었습니다. 알려진 개인정보를 심어 둔 시험 사이트로 모델을 채점합니다.

Closed-loop MLOps pipeline for demand forecasting: FastAPI serving with prediction logging, Airflow monitoring a 14-day rolling RMSE to trigger retraining, leak-free chronological validation, and a promotion audit so a new model ships only if it beats production.

End-to-end Azure MLOps platform for fraud detection with Databricks, Azure ML, MLflow, GitHub Actions, CT/CD, drift monitoring, quality gates, blue/green deployment, rollback, and OIDC security.

Autonomous MLOps microservice learning localized microclimate bias in numerical weather forecasts.

This project integrates Airflow, EC2, MLFlow, and MLOps principles to deploy a robust pipeline for classifying medical MNIST images. Streamlit enables user-friendly image uploads, MLFlow handles model registry and inference, while Airflow automates data updates and model retraining on AWS EC2.

Closed-loop MLOps control plane: versioned data, gated training, canary releases, drift detection, automated retraining and rollback.