Deploy and serve models

Note

The managed MLflow integration with Databricks on Google Cloud requires Databricks Runtime for Machine Learning 8.1 or above.

Deploy models to production with MLflow

For information about deploying models with MLflow, see Log, load, register, and deploy MLflow Models. The following notebook illustrates how to use MLflow Model Registry to build, manage, and deploy a model.

MLflow Model Registry example

Run a Databricks job

You can create a Databricks job to run a notebook or JAR either immediately or on a scheduled basis.