Updates to fraud detection tutorial - OAI 2.15 review

2_save_model.ipynb

@@ -117,7 +117,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Use the function to upload the `models` folder in a rescursive fashion:"
+    "Use the function to upload the `models` folder in a recursive fashion:"
    ]
   },
   {

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

@@ -200,4 +200,4 @@ The result should be a `models/fraud/1/model.onnx` file in your S3 bucket which
 
 .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[Running a data science pipeline generated from Python code]

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

@@ -30,13 +30,21 @@ image::workbenches/create-workbench-form-image.png[Workbench image, 600]
 
 . Choose a small deployment.
 +
-image::workbenches/create-workbench-form-size.png[Workbench size,600]
+image::workbenches/create-workbench-form-size.png[Workbench size, 600]
+
+. Leave the *Accelerator* field with the default `None` selection.
++ 
+image::workbenches/create-workbench-form-accelerator.png[Workbench accelerator, 600]
 
 . Leave the default environment variables and storage options.
 +
 image::workbenches/create-workbench-form-env-storage.png[Workbench storage, 600]
 
-. Under *Data connections*, select *Use existing data connection* and select `My Storage` (the object storage that you configured previously) from the list.
+. Under *Data connections*, select *Use a data connection*.
++
+image::workbenches/create-workbench-form-use-data-connection.png[Use data connection, 600]
+
+. Select *Use existing data connection*, and then select `My Storage` (the object storage that you configured previously) from the list.
 +
 image::workbenches/create-workbench-form-data-connection.png[Data connection, 600]
 
@@ -57,4 +65,4 @@ image::workbenches/ds-project-workbench-list-edit.png[Workbench list edit, 250]
 
 .Next step
 
-xref:importing-files-into-jupyter.adoc[Importing the {deliverable} files into the Jupyter environment]
+xref:importing-files-into-jupyter.adoc[Importing the {deliverable} files into the Jupyter environment]

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

@@ -36,15 +36,14 @@ https://github.com/rh-aiservices-bu/fraud-detection.git
 +
 image::workbenches/jupyter-git-modal.png[Git Modal, 200]
 
-.. Check the *Include submodules* option.
-
 .. Check the *Include submodules* option, and then click *Clone*.
 
 .. In the file browser, double-click the newly-created *fraud-detection* folder.
 +
 image::workbenches/jupyter-file-browser.png[Jupyter file browser, 300]
 
-.. In the left navigation bar, click the *Git* icon and then, in the *Filter* field, enter *{git-version}*.
+.. In the left navigation bar, click the *Git* icon, and then click *Current Branch* to expand the branches and tags selector panel.
+... On the *Branches* tab, in the *Filter* field, enter *{git-version}*.
 +
 image::workbenches/jupyter-git-icon-version.png[Git Clone icon, 200]
 +

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

@@ -38,6 +38,7 @@ image::pipelines/dsp-pipline-view-runs.png[]
 . Click *Create run*.
 
 . On the *Create run* page, provide the following values:
+.. For *Experiment*, leave the default `Default` value. 
 .. For *Name*, type any name, for example `Run 1`.
 .. For *Pipeline*, select the pipeline that you uploaded.
 +
@@ -47,6 +48,6 @@ image::pipelines/pipeline-create-run-form.png[Create Pipeline Run form]
 
 . Click *Create* to create the run.
 +
-A new run starts immediately. The *Details* page shows a pipeline created in Python that is running in {productname-short}.
+A new run starts immediately. The run details page shows a pipeline created in Python that is running in {productname-short}.
 +
 image::pipelines/pipeline-run-in-progress.png[]

workshop/docs/modules/ROOT/pages/testing-the-model-api.adoc

@@ -12,7 +12,7 @@ Now that you've deployed the model, you can test its API endpoints.
 +
 image::model-serving/ds-project-model-inference-endpoint.png[Model inference endpoint]
 +
-If the *Inference endpoint* field contains an *Internal Service* link, click the link to open a text box that shows the URL.
+If the *Inference endpoint* field contains an *Internal endpoint details* link, click the link to open a text box that shows the URL details, and then take note of the *restUrl* value.
 
 . Return to the Jupyter environment and try out your new endpoint.
 +