added ref links

Author: s-polly

articles/machine-learning/tutorial-pipeline-python-sdk.md

@@ -108,6 +108,10 @@ ml_client = MLClient(
 )
 ```
 
+**Reference links:**
+- [MLClient](https://learn.microsoft.com/python/api/azure-ai-ml/azure.ai.ml.mlclient)
+- [DefaultAzureCredential](https://learn.microsoft.com/python/api/azure-identity/azure.identity.defaultazurecredential)
+
 > [!NOTE]
 > Creating MLClient won't connect to the workspace. The client initialization is lazy, it will wait for the first time it needs to make a call (this will happen in the next code cell).
 
@@ -121,6 +125,9 @@ ws = ml_client.workspaces.get(WS_NAME)
 print(ws.location, ":", ws.resource_group)
 ```
 
+**Reference links:**
+- [WorkspaceOperations.get](https://learn.microsoft.com/python/api/azure-ai-ml/azure.ai.ml.operations.workspaceoperations#azure-ai-ml-operations-workspaceoperations-get)
+
 ## Access the registered data asset
 
 Start by getting the data that you previously registered in [Tutorial: Upload, access and explore your data in Azure Machine Learning](tutorial-explore-data.md).
@@ -134,6 +141,9 @@ credit_data = ml_client.data.get(name="credit-card", version="initial")
 print(f"Data asset URI: {credit_data.path}")
 ```
 
+**Reference links:**
+- [DataOperations.get](https://learn.microsoft.com/python/api/azure-ai-ml/azure.ai.ml.operations.dataoperations#azure-ai-ml-operations-dataoperations-get)
+
 ## Create a job environment for pipeline steps
 
 So far, you've created a development environment on the compute instance, your development machine. You also need an environment to use for each step of the pipeline. Each step can have its own environment, or you can use some common environments for multiple steps.
@@ -199,6 +209,10 @@ print(
 )
 ```
 
+**Reference links:**
+- [Environment](https://learn.microsoft.com/python/api/azure-ai-ml/azure.ai.ml.entities.environment)
+- [EnvironmentOperations.create_or_update](https://learn.microsoft.com/python/api/azure-ai-ml/azure.ai.ml.operations.environmentoperations#azure-ai-ml-operations-environmentoperations-create-or-update)
+
 ## Build the training pipeline
 
 Now that you have all assets required to run your pipeline, it's time to build the pipeline itself.
@@ -318,6 +332,11 @@ data_prep_component = command(
 )
 ```
 
+**Reference links:**
+- [command](https://learn.microsoft.com/python/api/azure-ai-ml/azure.ai.ml#azure-ai-ml-command)
+- [Input](https://learn.microsoft.com/python/api/azure-ai-ml/azure.ai.ml.input)
+- [Output](https://learn.microsoft.com/python/api/azure-ai-ml/azure.ai.ml.output)
+
 Optionally, register the component in the workspace for future reuse.
 
 
@@ -332,6 +351,9 @@ print(
 )
 ```
 
+**Reference links:**
+- [MLClient.create_or_update](https://learn.microsoft.com/python/api/azure-ai-ml/azure.ai.ml.mlclient#azure-ai-ml-mlclient-create-or-update)
+
 ### Create component 2: training (using yaml definition)
 
 The second component that you create consumes the training and test data, train a tree based model and return the output model. Use Azure Machine Learning logging capabilities to record and visualize the learning progress.
@@ -505,6 +527,10 @@ print(
 )
 ```
 
+**Reference links:**
+- [load_component](https://learn.microsoft.com/python/api/azure-ai-ml/azure.ai.ml#azure-ai-ml-load-component)
+- [MLClient.create_or_update](https://learn.microsoft.com/python/api/azure-ai-ml/azure.ai.ml.mlclient#azure-ai-ml-mlclient-create-or-update)
+
 ### Create the pipeline from components
 
 Now that both your components are defined and registered, you can start implementing the pipeline.
@@ -557,6 +583,11 @@ def credit_defaults_pipeline(
     }
 ```
 
+**Reference links:**
+- [dsl.pipeline](https://learn.microsoft.com/python/api/azure-ai-ml/azure.ai.ml.dsl#azure-ai-ml-dsl-pipeline)
+- [Input](https://learn.microsoft.com/python/api/azure-ai-ml/azure.ai.ml.input)
+- [Output](https://learn.microsoft.com/python/api/azure-ai-ml/azure.ai.ml.output)
+
 Now use your pipeline definition to instantiate a pipeline with your dataset, split rate of choice and the name you picked for your model.
 
 
@@ -572,6 +603,9 @@ pipeline = credit_defaults_pipeline(
 )
 ```
 
+**Reference links:**
+- [Input](https://learn.microsoft.com/python/api/azure-ai-ml/azure.ai.ml.input)
+
 ## Submit the job 
 
 It's now time to submit the job to run in Azure Machine Learning. This time you use `create_or_update`  on `ml_client.jobs`.
@@ -591,6 +625,10 @@ pipeline_job = ml_client.jobs.create_or_update(
 ml_client.jobs.stream(pipeline_job.name)
 ```
 
+**Reference links:**
+- [JobOperations.create_or_update](https://learn.microsoft.com/python/api/azure-ai-ml/azure.ai.ml.operations.joboperations#azure-ai-ml-operations-joboperations-create-or-update)
+- [JobOperations.stream](https://learn.microsoft.com/python/api/azure-ai-ml/azure.ai.ml.operations.joboperations#azure-ai-ml-operations-joboperations-stream)
+
 You can track the progress of your pipeline, by using the link generated in the previous cell. When you first select this link, you might see that the pipeline is still running. Once it's complete, you can examine each component's results.
 
 Double-click the **Train Credit Defaults Model** component.