updates for broken links

JustPies · JustPies · commit ff36281e4395 · 2025-07-15T10:42:13.000-04:00
diff --git a/articles/machine-learning/data-science-virtual-machine/dsvm-tutorial-resource-manager.md b/articles/machine-learning/data-science-virtual-machine/dsvm-tutorial-resource-manager.md
@@ -28,7 +28,7 @@ If your environment meets the prerequisites and you know how to use ARM template
 
 ## Prerequisites
 
-* An Azure subscription. If you don't have an Azure subscription, create a [free account](https://azure.microsoft.com/free/services/machine-learning/) before you begin.
+* An Azure subscription. If you don't have an Azure subscription, create a [free account](https://azure.microsoft.com/products/machine-learning/) before you begin.
 
 * You need the [Azure CLI](/cli/azure/install-azure-cli) to use the CLI commands in this document from your **local environment**.
 
diff --git a/articles/machine-learning/data-science-virtual-machine/reference-ubuntu-vm.md b/articles/machine-learning/data-science-virtual-machine/reference-ubuntu-vm.md
@@ -28,7 +28,7 @@ available in the `py38_pytorch` environment.
 
 H2O is a fast, in-memory, distributed machine learning and predictive analytics platform. A Python package is installed in both the root and py35 Anaconda environments. An R package is also installed.
 
-To open H2O from the command line, run `java -jar /dsvm/tools/h2o/current/h2o.jar`. You can configure various available[command-line options](http://docs.h2o.ai/h2o/latest-stable/h2o-docs/starting-h2o.html#from-the-command-line). Browse to the Flow web UI to `http://localhost:54321` to get started. JupyterHub offers sample notebooks.
+To open H2O from the command line, run `java -jar /dsvm/tools/h2o/current/h2o.jar`. You can configure various available command-line options. Browse to the Flow web UI to `http://localhost:54321` to get started. JupyterHub offers sample notebooks.
 
 ### TensorFlow
 
diff --git a/articles/machine-learning/how-to-access-data-interactive.md b/articles/machine-learning/how-to-access-data-interactive.md
@@ -655,7 +655,7 @@ df.head()
 > [!TIP]
 > Pandas is not designed to handle large datasets. Pandas can only process data that can fit into the memory of the compute instance.
 >
-> For large datasets, we recommend use of Azure Machine Learning managed Spark. This provides the [PySpark Pandas API](https://spark.apache.org/docs/latest/api/python/user_guide/pandas_on_spark/index.html).
+> For large datasets, we recommend use of Azure Machine Learning managed Spark. This provides the [Pandas API on Spark](https://spark.apache.org/docs/latest/api/python/tutorial/pandas_on_spark/index.html).
 
 You might want to iterate quickly on a smaller subset of a large dataset before scaling up to a remote asynchronous job. `mltable` provides in-built functionality to get samples of large data using the [take_random_sample](/python/api/mltable/mltable.mltable.mltable#mltable-mltable-mltable-take-random-sample) method:
 

Original file line number	Diff line number	Diff line change
`@@ -655,7 +655,7 @@ df.head()`
`655`	`655`	`> [!TIP]`
`656`	`656`	`> Pandas is not designed to handle large datasets. Pandas can only process data that can fit into the memory of the compute instance.`
`657`	`657`	`>`
`658`		`-> For large datasets, we recommend use of Azure Machine Learning managed Spark. This provides the [PySpark Pandas API](https://spark.apache.org/docs/latest/api/python/user_guide/pandas_on_spark/index.html).`
	`658`	`+> For large datasets, we recommend use of Azure Machine Learning managed Spark. This provides the [Pandas API on Spark](https://spark.apache.org/docs/latest/api/python/tutorial/pandas_on_spark/index.html).`
`659`	`659`
`660`	`660`	You might want to iterate quickly on a smaller subset of a large dataset before scaling up to a remote asynchronous job. `mltable` provides in-built functionality to get samples of large data using the [take_random_sample](/python/api/mltable/mltable.mltable.mltable#mltable-mltable-mltable-take-random-sample) method:
`661`	`661`