edits

Author: v-albemi

articles/machine-learning/how-to-create-component-pipelines-cli.md

@@ -53,9 +53,9 @@ First, you'll create a pipeline with components by using an example. Doing so gi
 
 In the `cli/jobs/pipelines-with-components/basics` directory of the [`azureml-examples` repository](https://github.com/Azure/azureml-examples), go to the `3b_pipeline_with_data` subdirectory. There are three types of files in this directory. These are the files that you need to create when you build your own pipeline.
 
-- **pipeline.yml**. This YAML file defines the machine learning pipeline. It describes how to break a full machine learning task into a multistep workflow. For example, consider the simple machine learning task of using historical data to train a sales forecasting model. You might want to build a sequential workflow that contains data processing, model training, and model evaluation steps.  Each step is a component that has a well-defined interface and can be developed, tested, and optimized independently. The pipeline YAML also defines how the child steps connect to other steps in the pipeline. For example, the model training step generates a model file and the model file is passed to a model evaluation step.
+- **pipeline.yml**. This YAML file defines the machine learning pipeline. It describes how to break a full machine learning task into a multistep workflow. For example, consider the simple machine learning task of using historical data to train a sales forecasting model. You might want to build a sequential workflow that contains data processing, model training, and model evaluation steps. Each step is a component that has a well-defined interface and can be developed, tested, and optimized independently. The pipeline YAML also defines how the child steps connect to other steps in the pipeline. For example, the model training step generates a model file and the model file is passed to a model evaluation step.
 
-- **component.yml**.  These YAML files define the components. They contain the following information:
+- **component.yml**. These YAML files define the components. They contain the following information:
   - Metadata: Name, display name, version, description, type, and so on. The metadata helps to describe and manage the component.
   - Interface: Inputs and outputs. For example, a model training component takes training data and number of epochs as input and generates a trained model file as output. After the interface is defined, different teams can develop and test the component independently.
   - Command, code, and environment: The command, code, and environment to run the component. The command is the shell command to run the component. The code usually refers to a source code directory. The environment can be an Azure Machine Learning environment (curated or customer created), Docker image, or conda environment.  
@@ -117,7 +117,7 @@ The table describes the most commonly used fields of the pipeline YAML schema. T
 |`inputs`|A dictionary of inputs to the pipeline job. The key is a name for the input within the context of the job, and the value is the input value. You can reference these pipeline inputs by the inputs of an individual step job in the pipeline by using the `${{ parent.inputs.<input_name> }}` expression.|
 |`outputs`|A dictionary of output configurations of the pipeline job. The key is a name for the output in the context of the job, and the value is the output configuration. You can reference these pipeline outputs by the outputs of an individual step job in the pipeline by using the `${{ parents.outputs.<output_name> }}` expression. |
 
-The *3b_pipeline_with_data* example, contains a three-step pipeline.
+The *3b_pipeline_with_data* example contains a three-step pipeline.
 
 - The three steps are defined under `jobs`. All three steps are of type `command`. Each step's definition is in a corresponding `component*.yml` file. You can see the component YAML files in the *3b_pipeline_with_data* directory. `componentA.yml` is described in the next section.
 - This pipeline has data dependency, which is common in real-world pipelines. Component A takes data input from a local folder under `./data` (lines 18-21) and passes its output to component B (line 29). Component A's output can be referenced as `${{parent.jobs.component_a.outputs.component_a_output}}`.