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Author: Blackmist

articles/machine-learning/concept-expressions.md

@@ -17,7 +17,7 @@ ms.custom: cliv2, sdkv2
 
 With Azure Machine Learning SDK and CLI v2, you can use _expressions_ when a value may not be known when you're authoring a job or component. When you submit a job or call a component, the expression is evaluated and the value is substituted.
 
-The format for an expression is `${{ <expression> }}`. Some expressions are evaluated on the _client_, when submitting the job or component. Other expressions are evaluated on the _server_ &emdash; the compute where the job or component is running.
+The format for an expression is `${{ <expression> }}`. Some expressions are evaluated on the _client_, when submitting the job or component. Other expressions are evaluated on the _server_ (the compute where the job or component is running.)
 
 ## Client expressions
 
@@ -26,7 +26,7 @@ The format for an expression is `${{ <expression> }}`. Some expressions are eval
 
 | Expression | Description | Scope |
 | ---- | ---- | ---- |
-| `${{inputs.<input_name>}}` | References to an input data asset. | Works for all jobs. |
+| `${{inputs.<input_name>}}` | References to an input data asset or model. | Works for all jobs. |
 | `${{outputs.<output_name>}}` | References to an output data asset. | Works for all jobs. |
 | `${{search_space.<hyperparameter>}}` | References the hyperparameters to use in a sweep job. The hyperparameter values for each trial are selected based on the `search_space`. | Sweep jobs only. |
 | `${{parent.inputs.<input_name>}}` | Binds the inputs of a child job (pipeline step) in a pipeline to the inputs of the top-level parent pipeline job. | Pipeline jobs only. |
@@ -46,4 +46,5 @@ For more information on these expressions, see the following articles and exampl
 * [Hyperparameter tuning a model](how-to-tune-hyperparameters.md)
 * [Tutorial: ML pipelines with Python SDK v2](tutorial-pipeline-python-sdk.md)
 * [Create and run component-based ML pipelines (CLI)](how-to-create-component-pipelines-cli.md)
-* [Example: Iris batch prediction notebook](https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/parallel/2a_iris_batch_prediction/iris_batch_prediction.ipynb)
+* [Example: Iris batch prediction notebook](https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/parallel/2a_iris_batch_prediction/iris_batch_prediction.ipynb)
+* [Example: Pipeline YAML file](https://github.com/Azure/azureml-examples/blob/main/cli/jobs/pipelines-with-components/nyc_taxi_data_regression/pipeline.yml)

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

@@ -78,6 +78,7 @@ Azure Machine Learning pipelines are a powerful facility that begins delivering
 + [Define pipelines with Designer](./how-to-create-component-pipelines-ui.md)
 + Try out [CLI v2 pipeline example](https://github.com/Azure/azureml-examples/tree/sdk-preview/cli/jobs/pipelines-with-components)
 + Try out [Python SDK v2 pipeline example](https://github.com/Azure/azureml-examples/tree/main/sdk/python/jobs/pipelines)
++ Learn about [SDK and CLI v2 expressions](concept-expressions.md) that can be used in a pipeline.
 :::moniker-end
 :::moniker range="azureml-api-1"
 + [Create and run machine learning pipelines](v1/how-to-create-machine-learning-pipelines.md)

articles/machine-learning/how-to-train-model.md

@@ -193,7 +193,7 @@ In the above examples, you configured:
 - `code` - path where the code to run the command is located
 - `command` -  command that needs to be run
 - `environment` - the environment needed to run the training script. In this example, we use a curated or ready-made environment provided by Azure Machine Learning called `AzureML-lightgbm-3.2-ubuntu18.04-py37-cpu`. We use the latest version of this environment by using the `@latest` directive. You can also use custom environments by specifying a base docker image and specifying a conda yaml on top of it.
-- `inputs` - dictionary of inputs using name value pairs to the command. The key is a name for the input within the context of the job and the value is the input value. Inputs are referenced in the `command` using the `${{inputs.<input_name>}}` expression. To use files or folders as inputs, you can use the `Input` class.
+- `inputs` - dictionary of inputs using name value pairs to the command. The key is a name for the input within the context of the job and the value is the input value. Inputs are referenced in the `command` using the `${{inputs.<input_name>}}` expression. To use files or folders as inputs, you can use the `Input` class. For more information, see [SDK and CLI v2 expressions](concept-expressions.md).
 
 For more information, see the [reference documentation](/python/api/azure-ai-ml/azure.ai.ml#azure-ai-ml-command).
 
@@ -212,7 +212,7 @@ The `az ml job create` command used in this example requires a YAML job definiti
 In the above, you configured:
 - `code` - path where the code to run the command is located
 - `command` - command that needs to be run
-- `inputs` - dictionary of inputs using name value pairs to the command. The key is a name for the input within the context of the job and the value is the input value. Inputs are referenced in the `command` using the `${{inputs.<input_name>}}` expression. 
+- `inputs` - dictionary of inputs using name value pairs to the command. The key is a name for the input within the context of the job and the value is the input value. Inputs are referenced in the `command` using the `${{inputs.<input_name>}}` expression. For more information, see [SDK and CLI v2 expressions](concept-expressions.md).
 - `environment` - the environment needed to run the training script. In this example, we use a curated or ready-made environment provided by Azure Machine Learning called `AzureML-sklearn-0.24-ubuntu18.04-py37-cpu`. We use the latest version of this environment by using the `@latest` directive. You can also use custom environments by specifying a base docker image and specifying a conda yaml on top of it.
 To submit the job, use the following command. The run ID (name) of the training job is stored in the `$run_id` variable:
 

articles/machine-learning/toc.yml

@@ -154,6 +154,8 @@
   # end v1
   - name: Azure Machine Learning CLI and Python SDK
     href: concept-v2.md
+  - name: CLI and Python SDK v2 expressions
+    href: concept-expressions.md
   - name: Manage environments with SDK and CLI (v2)
     displayName: environment
     href: how-to-manage-environments-v2.md

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

@@ -350,7 +350,7 @@ if __name__ == "__main__":
 
 Now that you have a script that can perform the desired task, create an Azure Machine Learning Component from it.
 
-Use the general purpose `CommandComponent` that can run command line actions. This command line action can directly call system commands or run a script. The inputs/outputs are specified on the command line via the `${{ ... }}` notation.
+Use the general purpose `CommandComponent` that can run command line actions. This command line action can directly call system commands or run a script. The inputs/outputs are specified on the command line via the `${{ ... }}` (expression) notation. For more information, see [SDK and CLI v2 expressions](concept-expressions.md).