# Setup MLflow in Neevcloud Instance
## Setup MLflow in NeevCloud
### What is MLflow?
MLflow is an open-source platform designed to manage the machine learning lifecycle. It includes features to:
* Track experiments and runs
* Package and share models
* Deploy models to various environments
* Monitor model performance
Now set the MLflow in Neevcloud
## **Setup Environment**
1. Ubuntu 22.04
2. Python3
3. Mlflow
Create a ubuntu-22 [instance](https://docs.neevcloud.com/neevcloud-products/computes/getting-started-launch-vms).
### **Update && Upgrade all the packages**
```
sudo apt-get update -y
sudo apt-get upgrade -y
```
### **Install python and set the environment**
```
sudo apt-get install -y python3-pip
```
The pip is the package installer for Python, which allows you to easily install and manage libraries and packages for your Python projects
```
sudo apt-get install -y python3-venv
```
The venv module in Python creates isolated environments for projects, preventing dependency conflicts, ensuring reproducibility, and allowing safe experimentation without affecting the system-wide Python installation.
### **Set up a packagesVirtual environment**
```
sudo python3 -m venv myenv
```
```
source myenv/bin/activate
```
### **Install necessary packages**
**Upgrade and install packages within the environment.**
```
pip install --upgrade pip
```
```
pip install --upgrade setuptools
pip install mlflow scikit-learn
```
MLFLOW\_TRACKING\_URI= sets the MLflow Tracking Server URL, enabling centralized logging and tracking of machine learning experiments for collaboration and consistency.
```
export MLFLOW_TRACKING_URI=http://0.0.0.0:5000
```
The command runs the MLflow server in the background, ensuring it continues running after logout, logs output to mlflow\.log, and sets up the tracking server with SQLite backend
```
nohup mlflow server --backend-store-uri sqlite:///mlflow.db --default-artifact-root ./mlruns --host 0.0.0.0 --port 5000 &> mlflow.log &
```
After running the command open your browser and log in with the IP (Your server IP) and **Port(5000)**
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