> For the complete documentation index, see [llms.txt](https://www.sparkitecture.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://www.sparkitecture.io/machine-learning/mlflow.md).

# MLflow

MLflow is an open source library by the Databricks team designed for managing the machine learning lifecycle. It allows for the creation of projects, tracking of metrics, and model versioning.

#### Install mlflow using pip

```python
pip install mlflow
```

{% hint style="warning" %}
MLflow can be used in any Spark environmnet, but the automated tracking and UI of MLflow is Databricks-Specific Functionality.
{% endhint %}

Track metrics and parameters

```python
import mlflow

## Log Parameters and Metrics from your normal MLlib run
with mlflow.start_run():
  # Log a parameter (key-value pair)
  mlflow.log_param("alpha", 0.1)

  # Log a metric; metrics can be updated throughout the run
  mlflow.log_metric("AUC", 0.871827)
  mlflow.log_metric("F1", 0.726153)
  mlflow.log_metric("Precision", 0.213873)
```

MLflow GitHub: <https://github.com/mlflow/mlflow/>