The managed MLflow integration with Databricks on Google Cloud requires Databricks Runtime for Machine Learning 9.1 LTS or above.
This notebook provides a quick overview of machine learning model training on Databricks. To train models, you can use libraries like scikit-learn that are preinstalled in Databricks Runtime ML. In addition, you can use MLflow to track the trained models, and Hyperopt with SparkTrials to scale hyperparameter tuning.
In this tutorial, you train a simple classification model using MLflow to track model development and Hyperopt to improve the model’s performance. For more details on productionizing machine learning on Databricks including model lifecycle management and model inference, see the ML end-to-end example.
For additional example notebooks to get started quickly on Databricks, see Tutorials: Get started with ML.
Databricks Runtime 7.5 ML or above.