Skip to content

Commit e71a12a

Browse files
committed
grammar
1 parent 380f72e commit e71a12a

File tree

1 file changed

+2
-2
lines changed

1 file changed

+2
-2
lines changed

articles/machine-learning/service/how-to-use-mlflow.md

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -184,7 +184,7 @@ ws = Workspace.get(name=workspace_name,
184184

185185
#### Connect your Azure Databricks and Azure Machine Learning workspaces
186186

187-
On the [Azure portal](https://ms.portal.azure.com) you can link your Azure Databricks (ADB) workspace to a new or existing Azure Machine Learning workspace. To do so, navigate to your ADB workspace and select the **Link Azure Machine Learning workspace** button on the bottom right. Linking your workspaces enables you to track your experiment data in the Azure Machine Learning workspace.
187+
On the [Azure portal](https://ms.portal.azure.com), you can link your Azure Databricks (ADB) workspace to a new or existing Azure Machine Learning workspace. To do so, navigate to your ADB workspace and select the **Link Azure Machine Learning workspace** button on the bottom right. Linking your workspaces enables you to track your experiment data in the Azure Machine Learning workspace.
188188

189189
### Link MLflow tracking to your workspace
190190

@@ -203,7 +203,7 @@ import mlflow
203203
mlflow.log_metric('epoch_loss', loss.item())
204204
```
205205

206-
Instead of manually setting the tracking URI in every subsequent experiment notebook sessions on your clusters, do so automatically using this [Azure Machine Learning Tracking Cluster Init script](https://github.com/Azure/MachineLearningNotebooks/blob/3ce779063b000e0670bdd1acc6bc3a4ee707ec13/how-to-use-azureml/azure-databricks/linking/README.md).
206+
Instead of manually setting the tracking URI in every subsequent experiment notebook session on your clusters, do so automatically using this [Azure Machine Learning Tracking Cluster Init script](https://github.com/Azure/MachineLearningNotebooks/blob/3ce779063b000e0670bdd1acc6bc3a4ee707ec13/how-to-use-azureml/azure-databricks/linking/README.md).
207207

208208
When configured correctly, you are able to see your MLflow tracking data in Azure Machine Learning's REST API and all clients, and in Azure Databricks via the MLflow user interface or by using the MLflow client.
209209

0 commit comments

Comments
 (0)