This project uses MLflow UI to track, manage, and deploy machine learning models efficiently. Instead of manually keeping track of different experiments and model versions, MLflow makes everything organized and reproducible in one place
🔹 What MLflow UI Does for my project MLOps-Salary-project
- Track Experiments 📝 – Every model run is logged with metrics, parameters, and artifacts. No more guessing which experiment performed best!
- Model Registry 📦 – We store, version, and manage models here, so we can easily move them from development → staging → production
- Artifact Storage 📁 – MLflow saves logs, model files, and outputs, making it easy to check past results
- Deployment 🚀 – Models can be served directly from MLflow when ready for production
- Reproducibility 🔄 – Tracks code, dependencies, and environment for every run, so experiments are always reproducible