Introduction

Setting up a comprehensive environment for artificial intelligence development can be a complex and time-consuming task. It often involves configuring a machine learning manager, a distributed cloud-compatible file server, and a robust database. But what if I told you that you can achieve this in less than a minute? In this article, I’ll show you how to create a fully functional AI development environment using Docker Compose.

Why This Project?

The main motivation behind this project is to simplify the setup process for developers and data scientists. With this Docker Compose setup, you can quickly spin up an environment that includes an MLflow server for tracking experiments, MinIO for artifact storage, and PostgreSQL for database management—all while ensuring data persistence through Docker volumes.

Features

• One-Click Setup: Launch your AI environment with a single command.
• MLflow Integration: Manage and track your machine learning experiments effortlessly.
• MinIO for Artifact Storage: A distributed, cloud-compatible file server for your ML artifacts.
• PostgreSQL Database: Reliable database support with persistent data storage.
• Docker Volumes: Ensure your data persists across container restarts.

1.- Getting Started

git clone https://github.com/bygregonline/mlflow-docker-compose.git
cd mlflow-docker-compose

2.- Start the Environment

docker-compose up

Configuration

feel free to change passwords port and url in the .env file