updated link to new dataset doc

Author: Larry O'Brien

articles/machine-learning/concept-ml-pipelines.md

@@ -160,7 +160,7 @@ pipeline_run.wait_for_completion()
 
 The snippet starts with common Azure Machine Learning objects, a `Workspace`, a `Datastore`, a [ComputeTarget](https://docs.microsoft.com/python/api/azureml-core/azureml.core.computetarget?view=azure-ml-py), and an `Experiment`. Then, the code creates the objects to hold `input_data` and `output_data`. The array `steps` holds a single element, a `PythonScriptStep` that will use the data objects and run on the `compute_target`. Then, the code instantiates the `Pipeline` object itself, passing in the workspace and steps array. The call to `experiment.submit(pipeline)` begins the Azure ML pipeline run. The call to `wait_for_completion()` blocks until the pipeline is finished. 
 
-To learn more about connecting your pipeline to your data, see the articles [Data access in Azure Machine Learning](concept-data.md) and [Moving data into and between ML pipeline steps (Python)](how-to-move-data-in-and-out-of-pipelines.md). 
+To learn more about connecting your pipeline to your data, see the articles [Data access in Azure Machine Learning](concept-data.md) and [Moving data into and between ML pipeline steps (Python)](how-to-move-data-in-out-of-pipelines.md). 
 
 ## Best practices when using pipelines
 

articles/machine-learning/how-to-create-your-first-pipeline.md

@@ -111,7 +111,7 @@ output_data1 = PipelineData(
     output_name="output_data1")
 ```
 
-More details and sample code for working with datasets and pipeline data can be found in [Moving data into and between ML pipeline steps (Python)](how-to-move-data-in-and-out-of-pipelines.md).
+More details and sample code for working with datasets and pipeline data can be found in [Moving data into and between ML pipeline steps (Python)](how-to-move-data-in-out-of-pipelines.md).
 
 ## Set up a compute target
 
@@ -355,7 +355,7 @@ The line `Run.get_context()` is worth highlighting. This function retrieves a `R
 ws = Run.get_context().experiment.workspace
 ```
 
-For more detail, including alternate ways to pass and access data, see [Moving data into and between ML pipeline steps (Python)](how-to-move-data-in-and-out-of-pipelines.md).
+For more detail, including alternate ways to pass and access data, see [Moving data into and between ML pipeline steps (Python)](how-to-move-data-in-out-of-pipelines.md).
 
 ## Submit the pipeline