RHOAIENG-14086: Tutorial updates 2.14 (#56)

* Tutorial updates 2.14

* adding back .adoc extenstions to xrefs

* addressing review comments

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

@@ -31,11 +31,11 @@ image::pipelines/wb-pipeline-panel-button-loc.png[Open Panel,400]
 +
 image::pipelines/wb-pipeline-properties-tab.png[Pipeline Properties Tab]
 
-.. In the *Pipeline Properties* panel, scroll down to *Generic Node Defaults* and *Runtime Image*. Set the value to `Tensorflow with Cuda and Python 3.9 (UBI 9)`.
+.. In the *Pipeline Properties* panel, scroll down to *Generic Node Defaults* and *Runtime Image*. Set the value to `Tensorflow with Cuda and Python 3.11 (UBI 9)`.
 +
 image::pipelines/wb-pipeline-runtime-image.png[Pipeline Runtime Image0, 400]
 
-. Select *File* -> *Save Python File*.
+. Select *File* -> *Save Pipeline*.
 
 == Add nodes to your pipeline
 
@@ -164,11 +164,11 @@ image::pipelines/wb-pipeline-kube-secret-form.png[Secret Form, 300]
 ** *Secret Name*: `aws-connection-my-storage`
 ** *Secret Key*: `AWS_S3_BUCKET`
 
-. Select *File* -> *Save Python File As* to save and rename the pipeline. For example, rename it to `My Train Save.pipeline`.
+. Select *File* -> *Save Pipeline As* to save and rename the pipeline. For example, rename it to `My Train Save.pipeline`.
 
 == 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 or the pipeline provided in the `6 Train Save.pipeline` file.
+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 in the provided `6 Train Save.pipeline` file.
 
 .Procedure
 
@@ -185,15 +185,19 @@ NOTE: If `Data Science Pipeline` is not available as a runtime configuration, yo
 
 . Return to your data science project and expand the newly created pipeline.
 +
-image::pipelines/dsp-pipeline-complete.png[]
+image::pipelines/dsp-pipeline-complete.png[Set File Dependency Value, 800]
 
-. Click *View runs* and then view the pipeline run in progress.
+. Click the action menu (⋮) and then select *View runs* from the list.
 +
-image::pipelines/pipeline-run-complete.png[]
+image::pipelines/dsp-view-run.png[Set File Dependency Value, 800]
+
+. Click on your run and then view the pipeline run in progress.
++
+image::pipelines/pipeline-run-complete.png[Set File Dependency Value, 800]
 
 The result should be a `models/fraud/1/model.onnx` file in your S3 bucket which you can serve, just like you did manually in the xref:preparing-a-model-for-deployment.adoc[Preparing a model for deployment] section.
 
 
 .Next step
 
-(optional) xref:running-a-pipeline-generated-from-python-code.adoc[Automating workflows with data science pipelines]
+(optional) xref:running-a-pipeline-generated-from-python-code.adoc[Automating workflows with data science pipelines]

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, 600]
+image::workbenches/ds-project-create-workbench.png[Create workbench button, 800]
 
 . Fill out the name and description.
 +

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

@@ -31,8 +31,8 @@ image::model-serving/create-model-server-form.png[Create model server form, 400]
 image::model-serving/ds-project-workbench-list-deploy.png[Create model server form]
 
 . In the form, provide the following values:
-.. For *Model Name*, type `fraud`.
-.. For *Model framework*, select `onnx-1`.
+.. For *Model deployment name*, type `fraud`.
+.. For *Model framework (name - version)*, select `onnx-1`.
 .. For *Existing data connection*, select `My Storage`.
 .. Type the path that leads to the version folder that contains your model file: `models/fraud`
 .. Leave the other fields with the default settings.

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

@@ -3,6 +3,7 @@
 
 {productname-short} single-model servers host only one model. You create a new model server and deploy your model to it.
 
+
 .Prerequisites
 
 * A user with `admin` privileges has enabled the single-model serving platform on your OpenShift cluster.
@@ -17,9 +18,9 @@ NOTE: Depending on how model serving has been configured on your cluster, you mi
 
 . In the *Single-model serving platform* tile, click *Deploy model*.
 . In the form, provide the following values:
-.. For *Model Name*, type `fraud`.
+.. For *Model deployment name*, type `fraud`.
 .. For *Serving runtime*, select `OpenVINO Model Server`.
-.. For *Model framework*, select `onnx-1`.
+.. For *Model framework (name - version)*, select `onnx-1`.
 .. For *Existing data connection*, select `My Storage`.
 .. Type the path that leads to the version folder that contains your model file: `models/fraud`
 .. Leave the other fields with the default settings.
@@ -30,7 +31,7 @@ image::model-serving/deploy-model-form-sm.png[Deploy model form for single-model
 
 .Verification
 
-Notice the loading symbol under the *Status* section. The symbol changes to a green checkmark when the deployment 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/setting-up-your-data-science-project.adoc

@@ -15,9 +15,9 @@ Note that it is possible to start a Jupyter notebook by clicking the *Launch sta
 +
 NOTE: If you are using the {org-name} Developer Sandbox, you are provided with a default data science project (for example, `myname-dev`). Select it and skip over the next step to the *Verification* section.
 
-. Enter a display name and description. Based on the display name, a resource name is automatically generated, but you can change if you prefer.
+. Enter a display name and description.
 +
-image::projects/ds-project-new-form.png[New data science project form, 300]
+image::projects/ds-project-new-form.png[New data science project form, 600]
 
 .Verification