Export and import models


The managed MLflow integration with Databricks on Google Cloud requires Databricks Runtime for Machine Learning 9.1 LTS or above.

To save models, use the MLflow functions log_model and save_model. Also see Log, load, register, and deploy MLflow Models.

You can also save models using their native APIs onto What is the Databricks File System (DBFS)?. For MLlib models, use ML Pipelines.

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.