MLflow Model Management Tutorial

Overview

Learn how to develop and deploy machine learning applications using MLflow for model tracking and management within Digitalhub. This tutorial demonstrates the complete lifecycle of ML models from training to serving.

What You'll Learn

• Setting up MLflow tracking in Digitalhub
• Model versioning and experiment tracking
• Model registry management
• Automated model deployment
• Integration between MLflow and Digitalhub workflows

Getting Started

1. Import the Jupyter notebook notebook-mflows-model.ipynb into your Coder instance
2. Execute each cell step by step following the instructions

Resources

• 📁 Source Files: digitalhub-tutorials/s4-mlflow
• 🔧 MLflow Documentation: Official MLflow documentation