針對機器學習應用程式,Databricks 建議使用執行適用於機器學習的 Databricks Runtime 的叢集。 請參閱使用 Databricks Runtime ML 建立叢集。 若要開始使用 Databricks 上的深度學習,請參閱: Azure Databricks 上深度學習的最佳做法 Databricks 上的深度學習
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These articles can help you with your machine learning, deep learning, and other data science workflows in Databricks.
Databricks AutoML allows you to quickly generate baseline models and notebooks to accelerate machine learning workflows.
These articles can help you with your machine learning, deep learning, and other data science workflows in Databricks.
Databricks Runtime 17.0 for Machine Learning provides a ready-to-go environment for machine learning and data science based on Databricks Runtime 17.0 (Beta). Databricks Runtime ML contains many popular machine learning libraries, including TensorFlow, PyTorch, and XGBoost. Databricks Runtime ML inclu...
Databricks Autologging (Public Preview)The Databricks Autologging Public Preview has been expanded to new regions. Databricks Autologging is a no-code solution that provides automatic experiment tracking for machine learning training sessions on Azure Databricks. With Databricks Autologging, model ...
Configuring infrastructure for deep learning applications can be difficult.Databricks Runtime for Machine Learningtakes care of that for you, with clusters that have built-in compatible versions of the most common deep learning libraries like TensorFlow, PyTorch, and Keras. ...
11.1 (EoS). Databricks Runtime ML contains many popular machine learning libraries, including TensorFlow, PyTorch, and XGBoost. Databricks Runtime ML includesAutoML, a tool to automatically train machine learning pipelines. Databricks Runtime ML also supports distributed deep learning training using ...
Organizations and developers that are seeking to leverage the power of machine learning (ML) and AI spend a significant amount of time building ML models and are seeking a method for streamlining their machine learning development lifecycle to track experiments, package code into reproducible runs, ...