Fix typos in how-to-create-component-pipelines-cli

Author: mastloui-msft

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

@@ -53,7 +53,7 @@ From the `cli/jobs/pipelines-with-components/basics` directory of the [`azureml-
   - Interface: inputs and outputs. For example, a model training component will take training data and number of epochs as input, and generate a trained model file as output. Once the interface is defined, different teams can develop and test the component independently.
   - Command, code & environment: the command, code and environment to run the component.    Command is the shell command to execute the component. Code usually refers to a source  code directory. Environment could be an AzureML environment(curated or customer created), docker image or conda environment.  
 
-- **component_src**: This is the source code directory for a specific component. It contains the source code that will be executed in the component. You can use your preferred lanuage(Python, R...). The code must be executed by a shell command. The source code can take a few inputs from shell command line to control how this step is going to be executed. For example, a training step may take training data, learning rate, number of epochs to control the training process. The argument of a shell command is used to pass inputs and outputs to the code. 
+- **component_src**: This is the source code directory for a specific component. It contains the source code that will be executed in the component. You can use your preferred language(Python, R...). The code must be executed by a shell command. The source code can take a few inputs from shell command line to control how this step is going to be executed. For example, a training step may take training data, learning rate, number of epochs to control the training process. The argument of a shell command is used to pass inputs and outputs to the code. 
 
  Now let's create a pipeline using the `3b_pipeline_with_data` example. We'll explain the detailed meaning of each file in following sections. 
  
@@ -114,7 +114,7 @@ In the *3b_pipeline_with_data* example, we've created a three steps pipeline.
 
 ### Read and write data in pipeline
 
-One common scenario is to read and write data in your pipeline. In AuzreML, we use the same schema to [read and write data](how-to-read-write-data-v2.md) for all type of jobs (pipeline job, command job, and sweep job). Below are pipeline job examples of using data for common scenarios.
+One common scenario is to read and write data in your pipeline. In AzureML, we use the same schema to [read and write data](how-to-read-write-data-v2.md) for all type of jobs (pipeline job, command job, and sweep job). Below are pipeline job examples of using data for common scenarios.
 
 - [local data](https://github.com/Azure/azureml-examples/tree/sdk-preview/cli/jobs/pipelines-with-components/basics/4a_local_data_input)
 - [web file with public URL](https://github.com/Azure/azureml-examples/blob/sdk-preview/cli/jobs/pipelines-with-components/basics/4c_web_url_input/pipeline.yml)
@@ -131,14 +131,14 @@ The most common used schema of the component YAML is described in below table. S
 
 |key|description|
 |------|------|
-|name|**Required**. Name of the component. Must be unique across the AzureML workspace. Must start with lowercase letter. Allow lowercase letters, numbers and understore(_). Maximum length is 255 characters.|
+|name|**Required**. Name of the component. Must be unique across the AzureML workspace. Must start with lowercase letter. Allow lowercase letters, numbers and underscore(_). Maximum length is 255 characters.|
 |display_name|Display name of the component in the studio UI. Can be non-unique within the workspace.|
 |command|**Required** the command to execute|
 |code|Local path to the source code directory to be uploaded and used for the component.|
 |environment|**Required**. The environment that will be used to execute the component.|
 |inputs|Dictionary of component inputs. The key is a name for the input within the context of the component and the value is the component input definition. Inputs can be referenced in the command using the ${{ inputs.<input_name> }} expression.|
 |outputs|Dictionary of component outputs. The key is a name for the output within the context of the component and the value is the component output definition. Outputs can be referenced in the command using the ${{ outputs.<output_name> }} expression.|
-|is_deterministic|Whether to reuse previous job's result if the component inputs not change. Default value is `true`, also known as resue by default. The common scenario to set it as `false` is to force reload data from a cloud storage or URL.|
+|is_deterministic|Whether to reuse the previous job's result if the component inputs did not change. Default value is `true`, also known as reuse by default. The common scenario when set as `false` is to force reload data from a cloud storage or URL.|
 
 For the example in *3b_pipeline_with_data/componentA.yml*, componentA has one data input and one data output, which can be connected to other steps in the parent pipeline. All the files under `code` section in component YAML will be uploaded to AzureML when submitting the pipeline job. In this example, files under `./componentA_src` will be uploaded (line 16 in *componentA.yml*). You can see the uploaded source code in Studio UI: double select the ComponentA step and navigate to Snapshot tab, as shown in below screenshot. We can see it's a hello-world script just doing some simple printing, and write current datetime to the `componentA_output` path. The component takes input and output through command line argument, and it's handled in the *hello.py* using `argparse`.