> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mindsdb.com/llms.txt
> Use this file to discover all available pages before exploring further.

# MindsDB and MLflow

MLflow allows you to create, train, and serve machine learning models, apart from other features, such as organizing experiments, tracking metrics, and more.

## How to Use MLflow Models in MindsDB

Here are the prerequisites for using MLflow-served models in MindsDB:

1. Train a model via a wrapper class that inherits from the `mlflow.pyfunc.PythonModel` class. It should expose the `predict()` method that returns the predicted output for some input data when called.

   <Warning>
     Please ensure that the Python version specified for Conda environment matches the one used to train the model.
   </Warning>

2. Start the MLflow server:

   ```bash theme={null}
   mlflow server -p 5001 --backend-store-uri sqlite:////path/to/mlflow.db --default-artifact-root ./artifacts --host 0.0.0.0
   ```

3. Serve the trained model:

   ```bash theme={null}
   mlflow models serve --model-uri ./model_folder_name
   ```

## Example

Let's create a model that registers an MLflow-served model as an AI Table:

```sql theme={null}
CREATE MODEL mindsdb.mlflow_model
PREDICT target
USING
    engine = 'mlflow',
    model_name = 'model_folder_name',                     -- replace the model_folder_name variable with a real value
    mlflow_server_url = 'http://0.0.0.0:5001/',           -- match the port number with the MLflow server (point 2 in the previous section)
    mlflow_server_path = 'sqlite:////path/to/mlflow.db',  -- replace the path with a real value (here we use the sqlite database)
    predict_url = 'http://localhost:5000/invocations';    -- match the port number that serves the trained model (point 3 in the previous section)
```

Here is how to check the models status:

```sql theme={null}
DESCRIBE mlflow_model;
```

Once the status is `complete`, we can query for predictions.

One way is to query for a single prediction using synthetic data in the `WHERE` clause.

```sql theme={null}
SELECT target
FROM mindsdb.mlflow_model
WHERE text = 'The tsunami is coming, seek high ground';
```

Another way is to query for batch predictions by joining the model with the data table.

```sql theme={null}
SELECT t.text, m.predict
FROM mindsdb.mlflow_model AS m
JOIN files.some_text as t;
```

Here, the data table comes from the `files` integration. It is joined with the model and predictions are made for all the records at once.

<Tip>
  **Get More Insights**

  Check out the article on [How to bring your own machine learning model to databases](https://medium.com/mindsdb/how-to-bring-your-own-machine-learning-model-to-databases-47a188d6db00) by [Patricio Cerda Mardini](https://medium.com/@paxcema) to learn more.
</Tip>