Editorial updates

Author: madeline-underwood

content/learning-paths/servers-and-cloud-computing/gh-runners/_index.md

@@ -17,7 +17,7 @@ learning_objectives:
 prerequisites:
     - A GitHub account with access to Arm-hosted GitHub runners.
     - A Docker Hub account for storing container images.
-    - Familiarity with ML and continuous integration and deployment (CI/CD) concepts.
+    - Familiarity with the concepts of ML and continuous integration and deployment (CI/CD).
 
 author_primary: Pareena Verma, Annie Tallund
 

content/learning-paths/servers-and-cloud-computing/gh-runners/background.md

@@ -10,15 +10,17 @@ layout: learningpathall
 
 In this Learning Path, you will learn how to automate an MLOps workflow using Arm-hosted GitHub runners and GitHub Actions. 
 
-You will learn how to do the following tasks:
+You will perform the following tasks:
 - Train and test a neural network model with PyTorch.
 - Compare the model inference time using two different PyTorch backends.
 - Containerize the model and save it to DockerHub.
 - Deploy the container image and use API calls to access the model.
 
 ## GitHub Actions
 
-GitHub Actions is a platform that automates software development workflows, including continuous integration and continuous delivery. Every repository on GitHub has an `Actions` tab as shown below:
+GitHub Actions is a platform that automates software development workflows, which includes Continuous Integration and Continuous Delivery (CI/CD). 
+
+Every repository on GitHub has an **Actions** tab as shown below:
 
 ![#actions-gui](images/actions-gui.png)
 
@@ -32,11 +34,11 @@ The machine running workflows is called a _runner_.
 
 ## Arm-hosted GitHub runners
 
-Hosted GitHub runners are provided by GitHub so you don't need to set up and manage cloud infrastructure. Arm-hosted GitHub runners use the Arm architecture so you can build and test software without cross-compiling or instruction emulation.
+Hosted GitHub runners are provided by GitHub, so you do not need to set up and manage cloud infrastructure. Arm-hosted GitHub runners use the Arm architecture so you can build and test software without the necessity for cross-compiling or instruction emulation.
 
 Arm-hosted GitHub runners enable you to optimize your workflows, reduce cost, and improve energy consumption. 
 
-Additionally, the Arm-hosted runners are preloaded with essential tools, making it easier for you to develop and test your applications.
+Additionally, the Arm-hosted runners are preloaded with essential tools, making it easier for to develop and test your applications.
 
 Arm-hosted runners are available for Linux and Windows. This Learning Path uses Linux.
 
@@ -66,22 +68,22 @@ jobs:
 
 ## Machine Learning Operations (MLOps)
 
-Machine learning use-cases have a need for reliable workflows to maintain performance and quality. 
+Machine learning use cases require reliable workflows to maintain both performance and quality of output. 
 
-There are many tasks that can be automated in the ML lifecycle. 
-- Model training and re-training
-- Model performance analysis
-- Data storage and processing
-- Model deployment
+There are tasks that can be automated in the ML lifecycle, such as: 
+- Model training and retraining.
+- Model performance analysis.
+- Data storage and processing.
+- Model deployment.
 
-Developer Operations (DevOps) refers to good practices for collaboration and automation, including CI/CD. The domain-specific needs for ML, combined with DevOps knowledge, creates the new term MLOps.
+Developer Operations (DevOps) refers to good practices for collaboration and automation, including CI/CD. MLOps describes the area of practice where the ML application development intersects with ML system deployment and operations.
 
 ## German Traffic Sign Recognition Benchmark (GTSRB)
 
 This Learning Path explains how to train and test a PyTorch model to perform traffic sign recognition. 
 
 You will learn how to use the GTSRB dataset to train the model. The dataset is free to use under the [Creative Commons](https://creativecommons.org/publicdomain/zero/1.0/) license. It contains thousands of images of traffic signs found in Germany. It has become a well-known resource to showcase ML applications. 
 
-The GTSRB dataset is also good for comparing performance and accuracy of different models and to compare and contrast different PyTorch backends. 
+The GTSRB dataset is also effective for comparing the performance and accuracy of both different models, and different PyTorch backends. 
 
 Continue to the next section to learn how to set up an end-to-end MLOps workflow using Arm-hosted GitHub runners.

content/learning-paths/servers-and-cloud-computing/gh-runners/train-test.md

@@ -6,30 +6,29 @@ weight: 3
 layout: learningpathall
 ---
 
-In this section, you will fork the example GitHub repository containing the project code and inspect the Python code for training and testing a neural network model.
-
 ## Fork the example repository
 
-Get started by forking the example repository.
+In this section, you will fork the example GitHub repository containing the project code.
 
-In a web browser, navigate to the repository at:
+Get started by forking the example repository. In a web browser, navigate to the repository at:
 
 ```bash
 https://github.com/Arm-Labs/gh_armrunner_mlops_gtsrb
 ```
-
-Fork the repository, using the `Fork` button:
+Fork the repository, using the **Fork** button:
 
 ![#fork](/images/fork.png)
 
 Create a fork within a GitHub Organization or Team where you have access to Arm-hosted GitHub runners. 
 
 {{% notice Note %}}
-If a repository with the same name `gh_armrunner_mlops_gtsrb` already exists in your Organization or Team you modify the repository name to make it unique.
+If a repository with the same name `gh_armrunner_mlops_gtsrb` already exists in your Organization or Team, you can modify the repository name to make it unique.
 {{% /notice %}}
 
 ## Learn about model training and testing
 
+In this section, you will inspect the Python code for training and testing a neural network model.
+
 Explore the repository using a browser to get familiar with code and the workflow files. 
 
 {{% notice Note %}}
@@ -42,15 +41,15 @@ The purpose is to provide an overview of the code used for training and testing
 
 In the `scripts` directory, there is a Python script called `train_model.py`. This script loads the GTSRB dataset, defines a neural network, and trains the model on the dataset.
 
-#### Data pre-processing
+#### Data preprocessing
 
 The first section loads the GTSRB dataset to prepare it for training. The GTSRB dataset is built into `torchvision`, which makes loading easier. 
 
-The transformations used when loading data are part of the pre-processing step, which makes the data uniform and ready to run through the extensive math operations of the ML model. 
+The transformations used when loading data are part of the preprocessing step, which makes the data uniform and ready to run through the extensive math operations of the ML model. 
 
-In accordance with common machine learning practices, data is separated into training and testing data to avoid over-fitting the neural network.
+In accordance with common machine learning practices, data is separated into training and testing data to avoid overfitting the neural network.
 
-Here is the code to load the dataset:
+Use this code to load the dataset:
 
 ```python
 transform = transforms.Compose([
@@ -67,9 +66,9 @@ train_loader = DataLoader(train_set, batch_size=64, shuffle=True)
 
 The next step is to define a class for the model, listing the layers used. 
 
-The model defines the forward-pass function used at training time to update the weights. Additionally, the loss function and optimizer for the model are defined.
+The model defines the forward pass function used at training time to update the weights. Additionally, the loss function and optimizer for the model are defined.
 
-Here is the code defining the model:
+Use this code that defines the model:
 
 ```python
 class TrafficSignNet(nn.Module):
@@ -101,7 +100,7 @@ A training loop performs the actual training.
 
 The number of epochs is arbitrarily set to 10 for this example. When the training is finished, the model weights are saved to a `.pth` file.
 
-Here is the code for the training loop:
+Use this code for the training loop:
 
 ```python
 num_epochs = 10
@@ -144,7 +143,7 @@ Testing is done by loading the model that was saved after training and preparing
 
 As in training, transformations are used to load the test data from the GTSRB dataset.
 
-Here is the code to load the model and the test data:
+Use this code to load the model and the test data:
 
 ```python
 model_path = args.model if args.model else './models/traffic_sign_net.pth'
@@ -167,7 +166,7 @@ test_loader = DataLoader(test_set, batch_size=64, shuffle=False)
 
 The testing loop passes each batch of test data through the model and compares predictions to the actual labels to calculate accuracy. 
 
-The accuracy is calculated as a percentage of correctly classified images. Both the accuracy and PyTorch profiler report is printed at the end of the script.
+The accuracy is calculated as a percentage of correctly classified images. Both the accuracy and PyTorch profiler reports are printed at the end of the script.
 
 Here is the testing loop with profiling: