# 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**](/tutorials/register-your-node.md), you can set it up by following the steps of deployments as documented [**here**](/tutorials/deploy-your-product.md).

Once the deployment is successful, MLflow is ready to use at [**http://localhost:5001/**](http://localhost:5001/) by default, as seen below.

<figure><img src="/files/t67nxXg24Uqd1EF2LdFm" alt=""><figcaption></figcaption></figure>


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# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.dyrector.io/features/templates/mlflow.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.