XGBoost is a popular machine learning library designed specifically for training decision trees and random forests. For information about installing XGBoost on Databricks Runtime, or installing a custom version on Databricks Runtime ML, see these instructions. You can train XGBoost models on an individual machine or in a distributed fashion.

Azure Databricks provides an environment that makes it easy to build, train, and deploy deep learning models at scale. Many deep learning libraries are available in Databricks Runtime ML, a machine learning runtime that provides a ready-to-go environment for machine learning and data science. For deep learning libraries not included in Databricks Runtime ML, you can either install libraries as a Databricks library or use init scripts to install libraries on clusters upon creation.

We are excited to announce the general availability (GA) of Databricks Runtime for Machine Learning, as part of the release of Databricks Runtime 5.3 ML. It offers native integration with popular ML/DL frameworks, such as scikit-learn, XGBoost, TensorFlow, PyTorch, Keras, Horovod, etc. In addition to pre-configuring these popular frameworks, DBR ML makes these frameworks easier to use, more reliable, and more performant. Since we introduced Databricks Runtime for Machine Learning in preview in June 2018, we've witnessed exponential adoption in terms of both total workloads and the number of users. Close to 1000 organizations have tried Databricks Runtime ML preview versions over the past ten months.