ENG-3998: Updates for project details redesign

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

@@ -11,7 +11,7 @@ A workbench is an instance of your development and experimentation environment.
 
 . Navigate to the project detail page for the data science project that you created in xref:setting-up-your-data-science-project.adoc[Setting up your data science project].
 
-. Click the *Create workbench* button.
+. Click the *Workbenches* tab, and then click the *Create workbench* button.
 +
 image::workbenches/ds-project-create-workbench.png[Create workbench button]
 
@@ -43,7 +43,7 @@ image::workbenches/create-workbench-form-button.png[Create workbench button]
 
 .Verification
 
-In the project details page, the status of the workbench changes from `Starting` to `Running`.
+In the *Workbenches* tab for the project, the status of the workbench changes from `Starting` to `Running`.
 
 image::workbenches/ds-project-workbench-list.png[Workbench list]
 

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

@@ -23,9 +23,7 @@ Create data connections to your two storage buckets.
 
 . In the {productname-short} dashboard, navigate to the page for your data science project.
 
-. Under *Components*, click *Data connections*.
-
-. Click *Add data connection*.
+. Click the *Data connections* tab, and then click *Add data connection*.
 +
 image::projects/ds-project-add-dc.png[Add data connection]
 
@@ -49,8 +47,7 @@ image::projects/ds-project-pipeline-artifacts-form.png[Add pipeline artifacts fo
 
 
 .Verification
-
-Check to see that your data connections are listed in the project.
+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]
 

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

@@ -9,9 +9,9 @@
 
 .Procedure
 
-. In the {productname-short} dashboard, navigate to the *Models and model servers* section of your project.
+. In the {productname-short} dashboard, navigate to the project details page and click the *Models* tab.
 +
-image::model-serving/ds-project-model-list-add.png[Models and model servers]
+image::model-serving/ds-project-model-list-add.png[Models]
 
 . In the *Multi-model serving platform* tile, click *Add model server*.
 

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

@@ -10,11 +10,11 @@
 
 .Procedure
 
-. In the {productname-short} dashboard, navigate to the *Models and model servers* section of your project.
+. In the {productname-short} dashboard, navigate to the project details page and click the *Models* tab.
 +
-image::model-serving/ds-project-model-list-add.png[Models and model servers]
+image::model-serving/ds-project-model-list-add.png[Models]
 
-. Under *Single-model serving platform*, click *Deploy model*.
+. In the *Single-model serving platform* tile, click *Deploy model*.
 . In the form, provide the following values:
 .. For *Model Name*, type `fraud`.
 .. For *Serving runtime*, select `OpenVINO Model Server`.

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

@@ -15,7 +15,7 @@ In this {deliverable}, you implement an example pipeline by using the JupyterLab
 
 . In the {productname-short} dashboard, click *Data Science Projects* and then select *Fraud Detection*.
 
-. Navigate to the *Pipelines* section.
+. Click the *Pipelines* tab.
 
 . Click *Configure pipeline server*.
 +
@@ -38,13 +38,13 @@ You must wait until the pipeline configuration is complete before you continue a
 
 .Verification
 
-Check the *Pipelines* page. Pipelines are enabled when the *Configure pipeline server* button no longer appears.
+Check the *Pipelines* tab for the project. Pipelines are enabled when the *Configure pipeline server* button no longer appears.
 
 image::projects/ds-project-create-pipeline-server-complete.png[Create pipeline server complete]
 
 [NOTE]
 ====
-If you have waited more than 5 minutes and the pipeline server configuration does not complete, you can try to delete the pipeline server and create it again. 
+If you have waited more than 5 minutes and the pipeline server configuration does not complete, you can try to delete the pipeline server and create it again.
 
 image::projects//ds-project-delete-pipeline-server.png[Delete pipeline server]
 ====
@@ -56,6 +56,3 @@ xref:creating-a-workbench.adoc[Creating a workbench and selecting a notebook ima
 //xref:automating-workflows-with-pipelines.adoc[Automating workflows with data science pipelines]
 
 //xref:running-a-pipeline-generated-from-python-code.adoc[Running a data science pipeline generated from Python code]
-
-
-

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

@@ -9,15 +9,15 @@ This {deliverable} does not delve into the details of how to use the SDK. Instea
 +
 * `7_get_data_train_upload.py` is the main pipeline code.
 * `get_data.py`, `train_model.py`, and `upload.py` are the three components of the pipeline.
-* `build.sh` is a script that builds the pipeline and creates the YAML file. 
+* `build.sh` is a script that builds the pipeline and creates the YAML file.
 +
 For your convenience, the output of the `build.sh` script is provided in the `7_get_data_train_upload.yaml` file. The `7_get_data_train_upload.yaml` output file is located in the top-level `fraud-detection` directory.
 
 . Right-click the `7_get_data_train_upload.yaml` file and then click *Download*.
 
 . Upload the `7_get_data_train_upload.yaml` file to {productname-short}.
 
-.. In the {productname-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. Click the *Pipelines* tab and then click *Import pipeline*.
 +
 image::pipelines/dsp-pipeline-import.png[]
 
@@ -52,8 +52,3 @@ A new run starts immediately and opens the run details page.
 image::pipelines/pipeline-run-in-progress.png[]
 
 There you have it:  a pipeline created in Python that is running in {productname-short}.
-
-
-
-
-

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

@@ -19,7 +19,7 @@ NOTE: If you want to connect to your own storage, see xref:creating-data-connect
 
 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:
 
-In the {productname-short} dashboard, select *Data Science Projects* and then hover your cursor over the *?* icon next to the project name. A text box appears with information about the project, including it's resource name:
+In the {productname-short} dashboard, select *Data Science Projects* and then click the *?* icon next to the project name. A text box appears with information about the project, including its resource name:
 
 image::projects/ds-project-list-resource-hover.png[Project list resource name]
 

workshop/docs/modules/ROOT/pages/setting-up-your-data-science-project.adoc

@@ -23,21 +23,22 @@ image::projects/ds-project-new-form.png[New data science project form]
 
 .Verification
 
-You can now see its initial state. There are five types of project components:
+You can now see its initial state. Individual tabs provide more information about the project components and project access permissions:
 
 image::projects/ds-project-new.png[New data science project]
 
 ** *Workbenches* are instances of your development and experimentation environment. They typically contain IDEs, such as JupyterLab, RStudio, and Visual Studio Code.
 
-** A *Cluster storage* is a volume that persists the files and data you're working on within a workbench. A workbench has access to one or more cluster storage instances.
+** *Pipelines* contain the data science pipelines that are executed within the project.
 
-** *Data connections* contain configuration parameters that are required to connect to a data source, such as an S3 object bucket.
+** *Models* allow you to quickly serve a trained model for real-time inference. You can have multiple model servers per data science project. One model server can host multiple models.
+
+** *Cluster storage* is a persistent volume that retains the files and data you're working on within a workbench. A workbench has access to one or more cluster storage instances.
 
-** *Pipelines* contain the Data Science pipelines that are executed within the project.
+** *Data connections* contain configuration parameters that are required to connect to a data source, such as an S3 object bucket.
 
-** *Models and model servers* allow you to quickly serve a trained model for real-time inference. You can have multiple model servers per data science project. One model server can host multiple models.
+** *Permissions* define which users and groups can access the project.
 
 .Next step
 
 xref:storing-data-with-data-connections.adoc[Storing data with data connections]
-

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

@@ -6,7 +6,7 @@ Now that you've deployed the model, you can test its API endpoints.
 
 .Procedure
 
-. In the {productname-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 click the *Models* tab.
 
 . Take note of the model's Inference endpoint. You need this information when you test the model API.
 +
@@ -24,4 +24,3 @@ If you deployed your model with single-model serving, follow the directions in `
 xref:automating-workflows-with-pipelines.adoc[Automating workflows with data science pipelines]
 
 xref:running-a-pipeline-generated-from-python-code.adoc[Running a data science pipeline generated from Python code]
-