An end-to-end MLOps pipeline(CI/CD/CT/CM) project for training, versioning, deploying, and monitoring machine learning models using FastAPI, Kubernetes, MLflow, DVC, Prometheus, and Grafana.

Data drift detection for machine learning using Evidently AI and Valohai. MLOps pipeline: preprocessing, training, drift monitoring and conditional retraining. Python, scikit-learn, California Housing example.

DriftRadar-Vision is an advanced, production-ready drift monitoring and adaptive retraining pipeline for vision models.

la partie Monitoring & Observabilité (User Stories 7.1 & 7.2) du projet de plateforme de scoring/prediction ML. Cette structure combine la surveillance des performances API (Prometheus + Grafana) et la détection de dérive des modèles (EvidentlyAI).

Project to deploy ML model using Docker and Kubernetes

MLOps project to predict 30-day hospital readmissions for BPJS patients in Indonesia and includes an interactive dashboard for regional and demographic analysis.

Enterprise Wind Power MLOps Platform: built with NREL dataset only using free tools like mlflow, docker, AWS(free tier), evidently ai.

This repository is used to serve and monitor the stock predictor model.

Testing Evidently AI open source python library

Airbnb property rental price prediction model in the city of Rio de Janeiro, Brazil.

End-to-end MLOps pipeline (CI/CD/CT/CM) | FastAPI + MLflow + DVC + Kubernetes + Prometheus + Grafana + EvidentlyAI on AWS EKS

A scalable, production-ready time series forecasting system for Walmart sales data with automated retraining, monitoring, and canary deployments.