Expanding on example

Author: kquinn1204

modules/rhoai-demo-app.adoc

@@ -88,7 +88,7 @@ In this section, you will perform an interactive classification using a Jupyter
 
 . Click *Create project*
 
-.. Enter a name for the project for example ¬my-classification-project` in the *Name* field and click *Create*.
+.. Enter a name for the project for example `my-classification-project` in the *Name* field and click *Create*.
 
 . Click on *Create a workbench*. Now you are ready to move to the next step to define the workbench.
 
@@ -126,9 +126,9 @@ Simplify data preparation in AI projects by automating the fetching of datasets
 
 . Clone the Interactive Image Classification Project from the GitHub repository using the following instructions:
 
-.. At the top of the JupyterLab interface, click on the "Git Clone" icon.
+.. At the top of the JupyterLab interface, click on the *Git Clone* icon.
 
-.. In the popup window, enter the URL of the GitHub repository in the "Git Repository URL" field:
+.. In the popup window, enter the URL of the GitHub repository in the *Git Repository URL* field:
 +
 [source,text]
 ----
@@ -141,16 +141,40 @@ https://github.com/redhat-developer-demos/openshift-ai.git
 
 . Open the Python Notebook in the JupyterLab Interface.
 +
-The JupyterLab interface is presented after uploading `kaggle.json` and cloning the `openshift-ai``repository shown the file browser on the left with  'openshift-ai' and '.kaggle.json
+The JupyterLab interface is presented after uploading `kaggle.json` and cloning the `openshift-ai` repository shown the file browser on the left with  `openshift-ai` and ``.kaggle.json`.`
 
-. Open `Interactive_Image_Classification_Notebook.ipynb`` in the `openshift-ai` directory and run the notebook, The notebook contains all necessary instructions and is self-documented.
+. Open `Interactive_Image_Classification_Notebook.ipynb` in the `openshift-ai` directory and run the notebook, the notebook contains all necessary instructions and is self-documented.
 
 . Run the cells in the Python Notebook as follows:
 
 .. Start by executing each cell in order by pressing the play button or using the keyboard shortcut "Shift + Enter" 
 
-.. Once you run the cell in Step 4, you should see an output as shown in Figure 12 below.
+.. Once you run the cell in Step 4, you should see an output as shown in the following screenshot.
++
+image::rhoai/predict-step4.png[Interactive Real-Time Data Streaming and Visualization]
+
+.. Running the cell in Step 5, produces an output of two images, one of a cat and one of a dog, with their respective predictions labeled as "Cat" and "Dog".
+
+.. Once the code in the cell is executed in Step 6, a predict button will appear as shown in screenshot below. The interactive session will display images with their predicted labels in real-time as the user clicks the "Predict" button. This dynamic interaction helps in understanding how well the model performs across a random set of images and provides insights into potential improvements for model training.
++
+image::rhoai/predict.png[Interactive Real-Time Image Prediction with Widgets]
 
-.. Running the cell in Step 5, produces an output of two images, one of a cat and one of a dog, with their respective predictions labeled as "Cat" and "Dog". as shown in Figure 14 below.
+## Addressing misclassification in your AI Model
+
+Misclassification in machine learning models can significantly hinder your model's accuracy and reliability. To combat this, it's crucial to verify dataset balance, align preprocessing methods, and tweak model parameters. These steps are essential for ensuring that your model not only learns well, but also generalizes well, to new, unseen data.
+
+. Adjust the Number of epochs to optimize training speed
++
+Changing the number of *epochs* can help you find the sweet spot where your model learns enough to perform well without overfitting. This is crucial for building a robust model that performs consistently.
 
-.. Once the code in the cell is executed in Step 6, a predict button will appear as shown in Figure 15 above. The interactive session will display images with their predicted labels in real-time as the user clicks the "Predict" button. This dynamic interaction helps in understanding how well the model performs across a random set of images and provides insights into potential improvements for model training.
+. Try different values for steps per epoch.
++
+Modifying *steps_per_epoch* affects how many batches of samples are used in one epoch. This can influence the granularity of the model updates and can help in dealing with imbalanced datasets or overfitting.
+
+For example make these modifications in your Notebook or another Python environment as part of *Step 3: Build and Train the Model*:
+
+[source,text]
+----
+# Adjust the number of epochs and steps per epoch
+model.fit(train_generator, steps_per_epoch=100, epochs=10)
+----

modules/rhoai-deploying.adoc

@@ -170,7 +170,7 @@ The {validated-patterns-op} is successfully installed in the relevant namespace.
 To know the cluster group name for the patterns that you want to deploy, check the relevant pattern-specific requirements.
 . Expand the *Git Config* section to reveal the options and enter the required information.
 . Leave *In Cluster Git Server* unchanged. 
-.. Change the *Target Repo* URL to your forked repository URL. For example, change `https://github.com/validatedpatterns/<pattern_name>` to `https://github.com/<your-git-username>/<pattern-name>`
+.. Change the *Target Repo* URL to your forked repository URL. For example, change https://github.com/validatedpatterns/<pattern_name> to https://github.com/<your-git-username>/<pattern-name>.
 .. Optional: You might need to change the *Target Revision* field. The default value is `HEAD`. However, you can also provide a value for a branch, tag, or commit that you want to deploy. For example, `v2.1`, `main`, or a branch that you created, `my-branch`.
 . Click *Create*.
 +
@@ -225,8 +225,6 @@ $ cp values-secret.yaml.template ~/values-secret-openshift-ai.yaml
 ----
 ====
 
-The deployment will not take long but it should deploy successfully.
-
 Alternatively you can deploy the OpenShift AI pattern by using the command line script `pattern.sh`. 
 
 [id="deploying-cluster-using-patternsh-file"]