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.
Databricks Runtime does not support open source MLeap. To use MLeap, you must create a cluster running Databricks Runtime ML, which has a custom version of MLeap preinstalled.
The following notebook shows an example of a model export workflow.