mlflow-ui

Setting up an MLflow Workspace with Docker

⚡ Supercharge your home with AI 🧠

Experiment tracking with MLFlow.

The MLflow TensorFlow Guide is an educational project. This project demonstrates how to build, train, and manage a TensorFlow machine learning model using MLflow, a powerful open-source platform for the end-to-end machine learning lifecycle.

Merges/Gathers and manages multiple web-server UIs into a simple single electron app listening on localhost or remote server(s). The initial aim of this electron app is to simplify DataScience/MachineLearning experiments by merging and managing all your web servers into a single app (e.g. tensorboard, jupyter notebook, MLFlow UI, NNI UI, etc.). …

Using MLflow to deploy your RAG pipeline, using LLamaIndex, Langchain and Ollama/HuggingfaceLLMs/Groq

Integration guide and example setup for using llm-jp with mLflow prompt engineering UI.

Host MLFlow Tracking Server and Model Registry as a containerized application on Kubernetes

Les entreprises perdent chaque année des clients sans toujours comprendre pourquoi. Ce projet vous permettra de suivre et gérer le churn grâce à MLflow, en versionnant les modèles et visualisant les métriques pour améliorer la fidélisation et le revenu client.

🌐 Language identification for Scandinavian languages

In this project, you will create an end-to-end Airflow pipeline, integrated with MLflow, for CodePro, an EdTech startup, to perform lead scoring and maximize profitability while minimizing the Customer Acquisition Cost (CAC). The assignment involves data collection, preprocessing, and periodic model retraining within Airflow, alongside development

A hands-on MLflow project demonstrating experiment tracking, model training, and lifecycle management using Scikit-learn, XGBoost, and Dagshub integration.

This repo has a complete demonstration of performing experiment tracking with mlflow

End-to-end MLOps pipeline featuring modular data loading, model training, evaluation, and experiment tracking with MLflow and SQLite.

Link to DagsHub repository :

Démonstration prête à l'emploi d'un pipeline ML automatisé avec Jenkins (CI/CD), MLflow (tracking & model registry), Docker (packaging) et Kubernetes (déploiement).

Practical project for time series prediction using recurrent neural networks (RNN) with Python.

End-to-end MLOps workflow for California Housing Price prediction using Python, Scikit-Learn, and MLflow for experiment tracking and metric logging.