doc-updates-peer-review (#21)

rh-aiservices-bu · Feb 27, 2024 · c19c0ff · c19c0ff
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diff --git a/workshop/docs/modules/ROOT/pages/automating-workflows-with-pipelines.adoc b/workshop/docs/modules/ROOT/pages/automating-workflows-with-pipelines.adoc
@@ -99,7 +99,7 @@ The secret is named `aws-connection-my-storage`.
 
 [NOTE] 
 ====
-If you named your data connection something other than `My Storage`, you can obtain the secret name in the {prodname-short} dashboard by hovering over the resource information icon *?* in the *Data Connections* tab. 
+If you named your data connection something other than `My Storage`, you can obtain the secret name in the {productname-short} dashboard by hovering over the resource information icon *?* in the *Data Connections* tab. 
 
 image::pipelines/dsp-dc-secret-name.png[My Storage Secret Name, 400]
 ====

diff --git a/workshop/docs/modules/ROOT/pages/enabling-data-science-pipelines.adoc b/workshop/docs/modules/ROOT/pages/enabling-data-science-pipelines.adoc
@@ -7,7 +7,9 @@ In this section, you prepare your {deliverable} environment so that you can use
 
 .Procedure
 
-. In the {prodname-short} dashboard, navigate to *Data Science Pipelines* -> *Pipelines*.
+. In the {productname-short} dashboard, click *Data Science Projects* and then select *Fraud Detection*.
+
+. Navigate to the *Pipelines* section.
 
 . Click *Configure pipeline server*.
 +

diff --git a/workshop/docs/modules/ROOT/pages/navigating-to-the-dashboard.adoc b/workshop/docs/modules/ROOT/pages/navigating-to-the-dashboard.adoc
@@ -5,7 +5,7 @@
 
 . After you log in to the OpenShift console, access the {productname-short} dashboard by clicking the application launcher icon on the header.
 +
-image::projects/ocp-console-ds-tile.png[{prodname-short} dashboard link]
+image::projects/ocp-console-ds-tile.png[{productname-short} dashboard link]
 
 . When prompted, log in to the {productname-short} dashboard by using your OpenShift credentials. {productname-short} uses the same credentials as OpenShift for the dashboard, notebooks, and all other components.
 +

diff --git a/...shop/docs/modules/ROOT/pages/running-a-pipeline-generated-from-python-code.adoc b/...shop/docs/modules/ROOT/pages/running-a-pipeline-generated-from-python-code.adoc
@@ -17,7 +17,7 @@ image::pipelines/wb-download.png[Download Pipeline YAML]
 
 . Upload the `7_get_data_train_upload.yaml` file to {productname-short}.
 
-.. In the {prodname-short} dashboard, navigate to your data science project page and then click *Import pipeline*.
+.. In the {productname-short} dashboard, navigate to your data science project page and then click *Import pipeline*.
 +
 image::pipelines/dsp-pipeline-import.png[]
 

diff --git a/workshop/docs/modules/ROOT/pages/testing-the-model-api.adoc b/workshop/docs/modules/ROOT/pages/testing-the-model-api.adoc
@@ -9,7 +9,7 @@ You can communicate directly with this internal service in the same way that an
 
 .Procedure
 
-. In the {prodname-short} dashboard, navigate to the project details page and scroll down to the *Models and model servers* section.
+. In the {productname-short} dashboard, navigate to the project details page and scroll down to the *Models and model servers* section.
 
 . Take note of the model's resource name (API endpoint name) and the internal service's grpcURL and restURL. You need this information when you test the model API.
 +