---
title: "Deploy MLflow :v3.10.1-full"
description: "MLflow Full version see more: www.oploy.eu"
category: "AI/ML"
url: https://railway.com/deploy/mlflow-full
---

# Deploy MLflow :v3.10.1-full

MLflow Full version see more: www.oploy.eu

**[Deploy MLflow :v3.10.1-full on Railway](https://railway.com/template/mlflow-full)**

- **Creator:** M.Behbahani
- **Category:** AI/ML
- **Total deploys:** 12

## Template content

### Postgres https://devicons.railway.app/i/postgresql.svg

- **Image:** ghcr.io/railwayapp-templates/postgres-ssl:17

### MLflow https://mlflow.org/docs/latest/images/logo-dark.svg

- **Source:** oploy-eu/mlflow-tracking-server
- **Public domain:** Yes

## Documentation

# Deploy and Host MLflow Full on Railway

MLflow Full (`mlflow:v3.10.1-full`) is a platform for managing the machine learning and GenAI lifecycle. It provides experiment tracking, model registry, artifact storage, and prompt observability in a unified interface. Teams can log parameters, metrics, prompts, and models while organizing experiments and managing versions of ML and LLM systems.

When deployed on Railway, MLflow runs as a tracking server accessible through a web interface. Experiment metadata is stored in a PostgreSQL database, while artifacts (models, logs, datasets, evaluation outputs) should be stored in an S3-compatible bucket for persistence.

---

## MLflow Artifact Storage

Currently, artifact storage is **not persistent**.

Artifacts are saved locally inside the container at:

```
/app/mlruns
```

Because Railway containers use **ephemeral storage**, artifacts may be lost after **redeploys or restarts**.

To enable **persistent artifact storage**, configure an **S3-compatible bucket** (AWS S3, Cloudflare R2, MinIO, etc.) using the following environment variables:

```env
BACKEND_S3=s3://mlflow-artifacts/

AWS_ACCESS_KEY_ID=
AWS_SECRET_ACCESS_KEY=
AWS_DEFAULT_REGION=
```

The MLflow server is started with:

```
--default-artifact-root ${BACKEND_S3:-$BACKEND_s3}
```

If `BACKEND_S3` is defined, MLflow automatically detects and uses it as the artifact bucket.

---

## Dependencies

- PostgreSQL database for experiment metadata
- S3-compatible storage bucket for artifacts

---

## Deployment References

**MLflow Documentation**  
https://mlflow.org/docs/latest

**Railway Deployment Platform**  
https://railway.app

**Oploy AI & Data Science Platform**  
https://www.oploy.eu


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