incorporate peer review feedback (#49)

* incorporate peer review feedback

* final peer review comments from downstream

workshop/docs/modules/ROOT/pages/automating-workflows-with-pipelines.adoc

@@ -65,13 +65,13 @@ image::pipelines/wb-pipeline-node-1.png[Select Node 1, 150]
 
 . Scroll down to the *File Dependencies* section and then click *Add*.
 +
-image::pipelines/wb-pipeline-node-1-file-dep.png[Add File Dependency, 400]
+image::pipelines/wb-pipeline-node-1-file-dep.png[Add File Dependency, 500]
 
 . Set the value to `data/*.csv` which contains the data to train your model.
 
 . Select the *Include Subdirectories* option and then click *Add*.
 +
-image::pipelines/wb-pipeline-node-1-file-dep-form.png[Set File Dependency Value, 300]
+image::pipelines/wb-pipeline-node-1-file-dep-form.png[Set File Dependency Value, 500]
 
 . Save the pipeline.
 
@@ -168,7 +168,7 @@ image::pipelines/wb-pipeline-kube-secret-form.png[Secret Form, 300]
 
 == Run the Pipeline 
 
-Upload the pipeline on your cluster and run it. You can do so directly from the pipeline editor. You can use your own newly created pipeline for this or `6 Train Save.pipeline`.
+Upload the pipeline on your cluster and run it. You can do so directly from the pipeline editor. You can use your own newly created pipeline or the pipeline provided in the `6 Train Save.pipeline` file.
 
 .Procedure
 

workshop/docs/modules/ROOT/pages/conclusion.adoc

@@ -5,10 +5,11 @@
 [.text-center.strong]
 == Conclusion
 
-Congratulations. In this {deliverable}, you learned how to incorporate data science,  artificial intelligence, and machine learning into an OpenShift development workflow.
+Congratulations. In this {deliverable}, you learned how to incorporate data science, artificial intelligence, and machine learning into an OpenShift development workflow.
 
 You used an example fraud detection model and completed the following tasks:
 
 * Explored a pre-trained fraud detection model by using a Jupyter notebook.
 * Deployed the model by using {productname-short} model serving.
 * Refined and trained the model by using automated pipelines.
+* Learned how to train the model by using Ray, a distributed computing framework.

workshop/docs/modules/ROOT/pages/creating-a-workbench.adoc

@@ -16,7 +16,7 @@ A workbench is an instance of your development and experimentation environment.
 
 . Click the *Workbenches* tab, and then click the *Create workbench* button.
 +
-image::workbenches/ds-project-create-workbench.png[Create workbench button, 300]
+image::workbenches/ds-project-create-workbench.png[Create workbench button, 600]
 
 . Fill out the name and description.
 +

workshop/docs/modules/ROOT/pages/creating-data-connections-to-storage.adoc

@@ -1,11 +1,11 @@
 [id='creating-data-connections-to-storage']
 = Creating data connections to your own S3-compatible object storage
 
-If you have existing S3-compatible storage buckets that you want to use for this {deliverable}, you must create a data connection to one storage bucket for saving your data and models and, if you want to complete the pipelines section of this {deliverable}, create another data connection to a different storage bucket for saving pipeline artifacts.
+If you have existing S3-compatible storage buckets that you want to use for this {deliverable}, you must create a data connection to one storage bucket for saving your data and models. If you want to complete the pipelines section of this {deliverable}, create another data connection to a different storage bucket for saving pipeline artifacts.
 
-NOTE: If you do not have your own s3-compatible storage, or if you want to use a disposable local Minio instance instead, skip this section and follow the steps in xref:running-a-script-to-install-storage.adoc[Running a script to install local object storage buckets and create data connections].   The provided script automatically completes the following tasks for you: creates a Minio instance in your project, creates two storage buckets in that Minio instance, creates two data connections in your project, one for each bucket and both using the same credentials, and installs required network policies for service mesh functionality. 
+NOTE: If you do not have your own s3-compatible storage, or if you want to use a disposable local Minio instance instead, skip this section and follow the steps in xref:running-a-script-to-install-storage.adoc[Running a script to install local object storage buckets and create data connections]. The provided script automatically completes the following tasks for you: creates a Minio instance in your project, creates two storage buckets in that Minio instance, creates two data connections in your project, one for each bucket and both using the same credentials, and installs required network policies for service mesh functionality. 
 
-.Prerequisite
+.Prerequisites
 
 To create data connections to your existing S3-compatible storage buckets, you need the following credential information for the storage buckets:
 
@@ -27,11 +27,11 @@ If you don't have this information, contact your storage administrator.
 +
 image::projects/ds-project-add-dc.png[Add data connection]
 
-.. Fill out the *Add data connection* form and name your connection *My Storage*. This connection is for saving your personal work, including data and models. 
+.. Complete the *Add data connection* form and name your connection *My Storage*. This connection is for saving your personal work, including data and models. 
 +
 NOTE: Skip the *Connected workbench* item. You add data connections to a workbench in a later section.
 +
-image::projects/ds-project-my-storage-form.png[Add my storage form, 400]
+image::projects/ds-project-my-storage-form.png[Add my storage form, 500]
 
 .. Click *Add data connection*.
 
@@ -41,11 +41,11 @@ NOTE: If you do not intend to complete the pipelines section of the {deliverable
 
 .. Click *Add data connection*.
 
-.. Fill out the form and name your connection *Pipeline Artifacts*.
+.. Complete the form and name your connection *Pipeline Artifacts*.
 +
 NOTE: Skip the *Connected workbench* item. You add data connections to a workbench in a later section.
 +
-image::projects/ds-project-pipeline-artifacts-form.png[Add pipeline artifacts form, 400]
+image::projects/ds-project-pipeline-artifacts-form.png[Add pipeline artifacts form, 500]
 
 .. Click *Add data connection*.
 
@@ -54,7 +54,7 @@ image::projects/ds-project-pipeline-artifacts-form.png[Add pipeline artifacts fo
 
 In the *Data connections* tab for the project, check to see that your data connections are listed.
 
-image::projects/ds-project-dc-list.png[List of project data connections, 400]
+image::projects/ds-project-dc-list.png[List of project data connections, 500]
 
 
 .Next steps

workshop/docs/modules/ROOT/pages/deploying-a-model-multi-model-server.adoc

@@ -3,7 +3,7 @@
 
 {productname-short} multi-model servers can host several models at once. You create a new model server and deploy your model to it.
 
-.Prerequisite
+.Prerequisites
 
 * A user with `admin` privileges has enabled the multi-model serving platform on your OpenShift cluster.
 
@@ -43,7 +43,7 @@ image::model-serving/deploy-model-form-mm.png[Deploy model from for multi-model
 
 .Verification
 
-Notice the loading symbol under the *Status* section. It will change to a green checkmark when the deployment is completes successfully. 
+Notice the loading symbol under the *Status* section. The symbol changes to a green checkmark when the deployment completes successfully. 
 
 image::model-serving/ds-project-model-list-status-mm.png[Deployed model status]
 

workshop/docs/modules/ROOT/pages/deploying-a-model-single-model-server.adoc

@@ -3,10 +3,7 @@
 
 {productname-short} single-model servers host only one model. You create a new model server and deploy your model to it.
 
-NOTE: Depending on how model serving has been configured on your cluster, you might see only one model serving platform option.
-
-
-.Prerequisite
+.Prerequisites
 
 * A user with `admin` privileges has enabled the single-model serving platform on your OpenShift cluster.
 
@@ -27,13 +24,13 @@ NOTE: Depending on how model serving has been configured on your cluster, you mi
 .. Type the path that leads to the version folder that contains your model file: `models/fraud`
 .. Leave the other fields with the default settings.
 +
-image::model-serving/deploy-model-form-sm.png[Deploy model from for single-model serving, 400]
+image::model-serving/deploy-model-form-sm.png[Deploy model form for single-model serving, 500]
 
 . Click *Deploy*.
 
 .Verification
 
-Notice the loading symbol under the *Status* section. It will change to a green checkmark when the deployment is completes successfully.
+Notice the loading symbol under the *Status* section. The symbol changes to a green checkmark when the deployment completes successfully. 
 
 image::model-serving/ds-project-model-list-status-sm.png[Deployed model status]
 

workshop/docs/modules/ROOT/pages/distributed-jobs-with-ray.adoc

@@ -7,7 +7,7 @@ This section demonstrates how you can use Ray to distribute the training of a ma
 
 In your notebook environment, open the `8_distributed_training.ipynb` file and follow the instructions directly in the notebook. The instructions guide you through setting authentication, creating Ray clusters, and working with jobs.
 
-Optionally, if you want to view the python code for this section, you can find it in the `ray-scripts/train_tf_cpu.py` directory. 
+Optionally, if you want to view the Python code for this section, you can find it in the `ray-scripts/train_tf_cpu.py` file. 
 
 image::distributed/jupyter-notebook.png[Jupyter Notebook]
 

workshop/docs/modules/ROOT/pages/enabling-data-science-pipelines.adoc

@@ -29,7 +29,7 @@ image::projects/ds-project-create-pipeline-server-form.png[Selecting the Pipelin
 
 . Click *Configure pipeline server*.
 
-. Wait until the spinner disappears and *Start by importing a pipeline* is displayed.
+. Wait until the loading spinner disappears and *Start by importing a pipeline* is displayed.
 +
 [IMPORTANT]
 ====

workshop/docs/modules/ROOT/pages/importing-files-into-jupyter.adoc

@@ -5,7 +5,7 @@
 
 The Jupyter environment is a web-based environment, but everything you do inside it happens on *{productname-long}* and is powered by the *OpenShift* cluster. This means that, without having to install and maintain anything on your own computer, and without disposing of valuable local resources such as CPU, GPU and RAM, you can conduct your Data Science work in this powerful and stable managed environment.
 
-.Prerequisite
+.Prerequisites
 
 You created a workbench, as described in xref:creating-a-workbench.adoc[Creating a workbench and selecting a Notebook image].
 

workshop/docs/modules/ROOT/pages/index.adoc

@@ -13,8 +13,9 @@ You will use an example fraud detection model to complete the following tasks:
 * Explore a pre-trained fraud detection model by using a Jupyter notebook.
 * Deploy the model by using {productname-short} model serving.
 * Refine and train the model by using automated pipelines.
+* Learn how to train the model by using Ray, a distributed computing framework.
 
-And you do not have to install anything on your own computer, thanks to https://www.redhat.com/en/technologies/cloud-computing/openshift/openshift-ai[{productname-long}].
+You do not have to install anything on your own computer, thanks to https://www.redhat.com/en/technologies/cloud-computing/openshift/openshift-ai[{productname-long}].
 
 == About the example fraud detection model
 

workshop/docs/modules/ROOT/pages/navigating-to-the-dashboard.adoc

@@ -25,7 +25,7 @@ The {productname-short} dashboard shows the *Home* page.
 
 NOTE: You can navigate back to the OpenShift console by clicking the application launcher to access the OpenShift console.
 
-image::projects/ds-console-ocp-tile.png[OCP console link, 250]
+image::projects/ds-console-ocp-tile.png[OCP console link, 400]
 
 For now, stay in the {productname-short} dashboard.
 

workshop/docs/modules/ROOT/pages/preparing-a-model-for-deployment.adoc

@@ -5,7 +5,7 @@ After you train a model, you can deploy it by using the {productname-short} mode
 
 To prepare a model for deployment, you must complete the following tasks:
 
-* Move the model from your workbench to your S3-compatible object storage. You use the data connection that you created in the xref:storing-data-with-data-connections.adoc[Storing data with data connections] section and upload the model from a notebook. 
+* Move the model from your workbench to your S3-compatible object storage. Use the data connection that you created in the xref:storing-data-with-data-connections.adoc[Storing data with data connections] section and upload the model from a notebook. 
 
 * Convert the model to the portable ONNX format. ONNX allows you to transfer models between frameworks with minimal preparation and without the need for rewriting the models.
 

workshop/docs/modules/ROOT/pages/running-a-pipeline-generated-from-python-code.adoc

@@ -3,7 +3,7 @@
 
 In the previous section, you created a simple pipeline by using the GUI pipeline editor. It's often desirable to create pipelines by using code that can be version-controlled and shared with others. The https://github.com/kubeflow/pipelines[Kubeflow pipelines (kfp)] SDK provides a Python API for creating pipelines. The SDK is available as a Python package that you can install by using the `pip install kfp` command. With this package, you can use Python code to create a pipeline and then compile it to YAML format. Then you can import the YAML code into {productname-short}.
 
-This {deliverable} does not delve into the details of how to use the SDK. Instead, it provides the files for you to view and upload.
+This {deliverable} does not describe the details of how to use the SDK. Instead, it provides the files for you to view and upload.
 
 . Optionally, view the provided Python code in your Jupyter environment by navigating to the `fraud-detection-notebooks` project's `pipeline` directory. It contains the following files:
 +

workshop/docs/modules/ROOT/pages/running-a-script-to-install-storage.adoc

@@ -15,7 +15,7 @@ IMPORTANT: The Minio-based Object Storage that the script creates is *not* meant
 
 NOTE: If you want to connect to your own storage, see xref:creating-data-connections-to-storage.adoc[Creating data connections to your own S3-compatible object storage].
 
-.Prerequisite
+.Prerequisites
 
 You must know the OpenShift resource name for your data science project so that you run the provided script in the correct project. To get the project's resource name:
 
@@ -37,7 +37,7 @@ oc apply -n <your-project-name/> -f https://github.com/rh-aiservices-bu/fraud-de
 
 . In the {productname-short} dashboard, click the application launcher icon and then select the *OpenShift Console* option.
 +
-image::projects/ds-project-ocp-link.png[OpenShift Console Link]
+image::projects/ds-project-ocp-link.png[OpenShift Console Link, 600]
 
 . In the OpenShift console, click *+* in the top navigation bar.
 +

workshop/docs/modules/ROOT/pages/running-code-in-a-notebook.adoc

@@ -19,7 +19,7 @@ You can run a code cell from the notebook interface or from the keyboard:
 
 * *From the user interface:* Select the cell (by clicking inside the cell or to the left side of the cell) and then click *Run* from the toolbar.
 +
-image::workbenches/run_button.png[Jupyter Run, 75]
+image::workbenches/run_button.png[Jupyter Run, 100]
 
 * *From the keyboard:* Press `CTRL` + `ENTER` to run a cell or press `SHIFT` + `ENTER` to run the cell and automatically select the next one.
 
@@ -35,7 +35,7 @@ Notebooks are so named because they are like a physical _notebook_: you can take
 
 Now that you know the basics, give it a try.
 
-.Prerequisite
+.Prerequisites
 
 * You have imported the {deliverable} files into your Jupyter environment as described in 
 xref:importing-files-into-jupyter.adoc[Importing the {deliverable} files into the Jupyter environment].

workshop/docs/modules/ROOT/pages/storing-data-with-data-connections.adoc

@@ -1,7 +1,7 @@
 [id='storing-data-with-data-connections']
 = Storing data with data connections
 
-Add data connections to workbenches to connect your project to data inputs and object storage buckets. A data connection is a resource that contains the configuration parameters needed to connect to an object storage bucket.
+Add data connections to workbenches if you want to connect your project to data inputs and object storage buckets. A data connection is a resource that contains the configuration parameters needed to connect to an object storage bucket.
 
 For this {deliverable}, you need two S3-compatible object storage buckets, such as Ceph, Minio, or AWS S3. You can use your own storage buckets or run a provided script that creates the following local Minio storage buckets for you:
 
@@ -12,4 +12,6 @@ Also, you must create a data connection to each storage bucket. You have two opt
 
 * If you want to use your own S3-compatible object storage buckets, create data connections to them as described in xref:creating-data-connections-to-storage.adoc[Creating data connections to your own S3-compatible object storage].
 
-* If you want to run a script that installs local Minio storage buckets and creates data connections to them, follow the steps in xref:running-a-script-to-install-storage.adoc[Running a script to install local object storage buckets and create data connections].
+* If you want to run a script that installs local Minio storage buckets and creates data connections to them, follow the steps in xref:running-a-script-to-install-storage.adoc[Running a script to install local object storage buckets and create data connections].
+
+NOTE: While it is possible for you to use one storage bucket for both purposes (storing models and data as well as storing pipeline artifacts), this tutorial follows best practice and uses separate storage buckets for each purpose.