MLeap ML model export

Important

This documentation has been retired and might not be updated. The products, services, or technologies mentioned in this content are no longer supported.

To export models for serving individual predictions, you can use MLeap, a common serialization format and execution engine for machine learning pipelines. MLeap supports serializing Apache Spark, scikit-learn, and TensorFlow pipelines into a bundle, so you can load and deploy trained models to make predictions with new data. You can import the exported models into both Spark and other platforms for scoring and predictions.

Note

Databricks Runtime does not support open source MLeap. To use MLeap, you must create a cluster running Databricks Runtime 13.3 LTS ML or below. These versions of Databricks Runtime ML have a custom version of MLeap preinstalled.

The following notebook shows an example of a model export workflow.

Example: Export and import models in Python

This notebook example demonstrates how to use MLeap to export models with MLlib.

MLeap export Python notebook

Open notebook in new tab Copy link for import