# MLflow [**MLflow**](https://mlflow.org/) **is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. The** [**Docker image**](https://hub.docker.com/r/adacotechjp/mlflow/tags) **of MLflow is available with the `2.4.0` tag.** After the node where you'd like to run MLflow is [**registered**](https://docs.dyrector.io/tutorials/register-your-node), you can set it up by following the steps of deployments as documented [**here**](https://docs.dyrector.io/tutorials/deploy-your-product). Once the deployment is successful, MLflow is ready to use at [**http://localhost:5001/**](http://localhost:5001/) by default, as seen below.