You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
{{ message }}
This repository was archived by the owner on Aug 10, 2021. It is now read-only.
Copy file name to clipboardExpand all lines: how-to-use-azureml/azure-databricks/README.md
+1-1Lines changed: 1 addition & 1 deletion
Display the source diff
Display the rich diff
Original file line number
Diff line number
Diff line change
@@ -25,7 +25,7 @@ Learn more about [how to use Azure Databricks as a development environment](http
25
25
You can use Azure Databricks as a compute target from [Azure Machine Learning Pipelines](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-ml-pipelines). Take a look at this notebook for details: [aml-pipelines-use-databricks-as-compute-target.ipynb](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/azure-databricks/databricks-as-remote-compute-target/aml-pipelines-use-databricks-as-compute-target.ipynb).
26
26
27
27
# Linked Azure Databricks and Azure ML Workspaces (Preview)
28
-
Customers can now link Azure Databricks and AzureML Workspaces to better enable MLOps scenarios by managing their tracking data in a single place - the Azure ML workspace.
28
+
Customers can now link Azure Databricks and AzureML Workspaces to better enable MLOps scenarios by [managing their tracking data in a single place when using the MLflow client](https://mlflow.org/docs/latest/tracking.html#mlflow-tracking) - the Azure ML workspace.
0 commit comments