Merge pull request rh-aiservices-bu#165 from rh-aiservices-bu/bugfix/…

…minor-typos

minor fixes and update images

content/modules/ROOT/pages/01-01-setting-stage.adoc

@@ -6,13 +6,13 @@ In order to make this lab seem more realistic, we will be describing this imagin
 == Situation
 :slide:
 
-* We all work for a large multi-national insurance company
+* We all work for a large multi-national insurance company:
 ** Our company is in the midst of digital transformation
 ** It is looking at modernizing practices and leveraging new technologies
 * A small team was asked to:
-** review the way insurance claims are currently being processed
-** provide advice on potential improvements
-* That team is currently very small (about 4 people)
+** Review the way insurance claims are currently being processed
+** Provide advice on potential improvements
+* That team is currently very small (about 4 people):
 ** Findings will be presented to the board
 ** If they are convincing, the team will be granted the resources to implement the recommendations
 * The next sections in this chapter are the materials that were presented to the board

content/modules/ROOT/pages/01-03-proposed-improvements.adoc

@@ -6,15 +6,15 @@ include::_attributes.adoc[]
 * Recommendations:
 ** Progressive, step-wise, implementation
 ** Use various AI/ML tools and techniques to **assist** the adjusters
-** (Completely replacing humans is extremely hard. Helping them is much easier)
+*** (Completely replacing humans is extremely hard. Helping them is much easier)
 ** Provide support for low-level, repetitive tasks
 *** Point out areas in need of review
 *** Help with parsing and data extraction
 * Goals:
-** bring average processing time from 7h/claim down to 2h/claim
-** reduce human error by 80%
-** improve fraud detection by 25%
-* Requirements
-** More precise measurements of performance
-**** at baseline
-**** after every change/improvement
+** Bring average processing time from 7h/claim down to 2h/claim
+** Reduce human error by 80%
+** Improve fraud detection by 25%
+* Requirements:
+** More precise measurements of performance:
+**** At baseline
+**** After every change/improvement

content/modules/ROOT/pages/01-04-examples-from-prototype.adoc

@@ -5,42 +5,42 @@ The examples below are what we hope to achieve through our prototype version of
 
 == Using an LLM for text summarization
 
-* Allows for faster reading by the claims adjuster
+* Allows for faster reading by the claims adjuster:
 +
 [.bordershadow]
 image::01/proto-summary.png[test image]
 
 == Using an LLM for information extraction
 
-* Extract key pieces of information for better population of database
+* Extract key pieces of information for better population of database:
 +
 [.bordershadow]
 image::01/proto-info-extract.png[ info extraction]
 
 == Using an LLM for sentiment analysis
 
-* Detect tone of text, and potentially act on it.
+* Detect tone of text, and potentially act on it:
 +
 [.bordershadow]
 image::01/proto-sentiment-analysis.png[]
 
 == Using image recognition to frame vehicle(s) in pictures
 
-* Analyse images provided by customer
+* Analyse images provided by customer:
 +
 [.bordershadow]
 image::01/proto-car-recog.png[]
 
 == Using image recognition to detect damage
 
-* Assessment of damage based on picture
+* Assessment of damage based on picture:
 +
 [.bordershadow]
 image::01/proto-accident-grading.png[]
 
 == Web Application to review/process claims
 
-* Have an application that ties in these tools together and enables users to process the incoming claims more efficiently
+* Have an application that ties in these tools together and enables users to process the incoming claims more efficiently:
 +
 [.bordershadow]
 image::01/proto-claims-processing-app.png[]

content/modules/ROOT/pages/02-01-getting-connected.adoc

@@ -39,11 +39,11 @@ We are now ready to start the lab.
 
 == Getting Support during RH1 In-Person Labs
 
-* In the room
+* In the room:
 ** Some very kind colleagues will be walking around in the room, to help and/or answer questions.
 ** If you run into a particular issue, call out to one of them and quietly explain what the issue is.
 ** If they are unsure or if it's likely to be a long explanation, they might ask you to "post the question in slack" instead. (see below)
-* Over slack
+* Over Slack:
 ** We have a dedicated Slack Channel where more colleagues (who kindly agreed to stay up late) will try to answer questions.
 ** Head over to the slack channel called https://redhat.enterprise.slack.com/archives/C066EQ8LWBS[#rh1-insurance-claims-lab,window=_blank]
 ** Post a message such as `I am userX and I have an issue with exercise number 2.4`

content/modules/ROOT/pages/02-02-creating-project.adoc

@@ -1,21 +1,21 @@
 = Creating your project and pipeline server
 include::_attributes.adoc[]
 
-As a preliminary step, each of you is going to
+As a preliminary step, each of you is going to:
 
-. Create a Data Science project
+. Create a Data Science project:
 ** this will help keep your things together
 
-. Create a Data Connection
+. Create a Data Connection:
 ** we need that for the pipeline server to store its artifacts
 
-. Deploy a Data Science Pipeline Server
+. Deploy a Data Science Pipeline Server:
 ** we will need one, and it's better to create it from the start
 
-. Launch a Workbench
+. Launch a Workbench:
 ** we will use it to review content and notebooks
 
-. Clone the git repo into your Workbench
+. Clone the git repo into your Workbench:
 ** this contains all the code from the prototype
 
 The instructions below will guide you through these steps. Follow them carefully.
@@ -99,12 +99,12 @@ image::02/data-connection.png[]
 
 It is highly recommended to create your pipeline server before creating a workbench. So let's do that now!
 
-* In your Data Science Project (DSP), click on **Create a pipeline Server**
+* In your Data Science Project (DSP), click on **Configure pipeline Server**
 +
 [.bordershadow]
 image::02/02-02-pipelineserver01.png[]
 
-* Select the Data Connection created earlier (**Shared Minio - pipelines**) and click the **Configure** button:
+* Select the Key Drop-Down with the option of *"Populate the form with credentials from your selected data connection"* using the Data Connection created earlier (**Shared Minio - pipelines**) and click the **Configure pipeline server** button:
 +
 [.bordershadow]
 image::02/02-02-pipelineserver02.png[]
@@ -117,4 +117,3 @@ image::02/02-02-pipelineserver03.png[]
 At this point, your pipeline server is ready and deployed.
 
 IMPORTANT: You need to **wait** until that screen is ready. If it's still spinning, wait for it to complete. If you continue and create your workbench **before** the pipeline server is ready, your workbench will not be able to submit pipelines to it.
-

content/modules/ROOT/pages/02-03-creating-workbench.adoc

@@ -3,8 +3,7 @@ include::_attributes.adoc[]
 
 == Launch a Workbench
 
-* Once the Data Connection and Pipeline Server are fully created
-* Create a workbench
+* Once the Data Connection and Pipeline Server are fully created, create a Workbench:
 +
 [.bordershadow]
 image::02/02-03-create-wb.png[]
@@ -17,9 +16,9 @@ image::02/02-03-create-wb.png[]
 +
 [.bordershadow]
 image::02/02-02-launch-workbench-01.png[]
-* You should not need to modify any other Workbench settings (such as Storage)
+NOTE: You should **not** need to modify any other Workbench settings (such as Storage)
 * Wait for your workbench to be fully started
-* Once it is, click the **Open** Link to connect to it.
+* Once it is, click the **Open** Link to connect to it:
 +
 [.bordershadow]
 image::02/02-03-open-link.png[]
@@ -30,7 +29,7 @@ image::02/02-03-open-link.png[]
 [.bordershadow]
 image::02/02-02-accept.png[]
 
-* Do so
+* Click on *Allow selected permissions*
 * You should now see this:
 +
 [.bordershadow]

content/modules/ROOT/pages/02-04-validating-env.adoc

@@ -37,7 +37,7 @@ If the output of this notebook looks suspicious, please inform the people leadin
 
 == Overall view
 
-This is a summarized visualization of how the environment is laid out.
+This is a summarized visualization of how the environment is laid out:
 
 [.bordershadow]
 image::02/ic-eng-diag.drawio.svg[]

content/modules/ROOT/pages/04-01-over-approach.adoc

@@ -4,16 +4,20 @@ include::_attributes.adoc[]
 As part of this prototype, we investigated the use of the YOLOv8 model.
 This model can be found online at https://www.yolov8.com[yolov8,window=_blank] and downloaded.
 
-We will first review its out-of-the-box capabilities. We will then fine-tune it to allow it to do more specialized work for us. Once we have a new, customized version of the model, we will deploy it in OpenShift AI Model Serving. Once that is done, we will send queries to it.
+== Image Processing Sections
+
+. We will first review its out-of-the-box capabilities. 
+. We will then fine-tune it to allow it to do more specialized work for us. 
+. Once we have a new, customized version of the model, we will deploy it in OpenShift AI Model Serving. 
+. Once that is done, we will send queries to it.
+
+== Image Processing Out-of-the-box capabilities
 
 Let's start by looking at a YOLOv8 model and explore how it works on static car images.
 
 [.bordershadow]
 image::04/sample-car-image.png[car image]
 
 - In your running workbench, navigate to the folder `insurance-claim-processing/lab-materials/04`.
-- Look for (and open) the notebook called `04-01-over-approach.ipynb`
-- Execute the cells of the notebook, and ensure you understand what is happening
-
-
-
+- Look for (and open) the notebook called`04-01-over-approach.ipynb`.
+- Execute the cells of the notebook, and ensure you understand what is happening.

content/modules/ROOT/pages/04-02-car-recog.adoc

@@ -23,6 +23,4 @@ image::04/box-identified-cars.png[identify cars]
 
 - In your running workbench, navigate to the folder `insurance-claim-processing/lab-materials/04`.
 - Look for (and open) the notebook called `04-02-car-recognition.ipynb`
-- Execute the cells of the notebook, and ensure you understand what is happening
-
-
+- Execute the cells of the notebook, and ensure you understand what is happening

content/modules/ROOT/pages/04-04-accident-recog.adoc

@@ -3,9 +3,9 @@ include::_attributes.adoc[]
 
 Now that we have retrained our model we can test it against some sample images.
 
-We have converted our model to onnx format and placed a copy within an S3 bucket.  We will test this version against some sample test images.
+We have converted our model to onnx format and placed a copy within an S3 bucket. We will test this version against some sample test images.
 
-Using the re-trained model, we will see that we are able to identify a severe car crash with 88% certainty, like in the below picture.
+Using the re-trained model, we will see that we are able to identify a severe car crash with 88% certainty, like in the below picture:
 
 [.bordershadow]
 image::04/retrained-model-results.png[retrained modelresults]

content/modules/ROOT/pages/04-05-model-serving.adoc

@@ -2,13 +2,13 @@
 include::_attributes.adoc[]
 
 . At this point, we need to deploy the model into RHOAI model serving.
-. We will create another data connection...
-.. with almost identical information
-.. but we will change the bucket name from `userX` to `models`
+. We will create another Data Connection...
+.. With almost identical information
+.. But we will change the bucket name from `userX` to `models`
 
 == Create a Data Connection
 
-* In your Data Science project, create a data connection that refers to the shared minio.
+* In your Data Science project, create a Data Connection that refers to the shared MinIO.
 * Here is the info you need to enter:
 ** Name:
 [.lines_space]
@@ -54,9 +54,9 @@ image::04/model-data-connection.png[model connection]
 
 == Create a Model Server
 
-In your project create a model server.
+In your project create a model server:
 
-* Click **Add model server**
+* In the "Multi-model serving platform" type of model, click **Add model server**:
 +
 [.bordershadow]
 image::04/add-model-server.png[]
@@ -118,7 +118,7 @@ image::04/add-model-server-config.png[]
 
 In your project, under **Models and model servers** select **Deploy model**.
 
-* Click **Deploy model**
+* Click **Deploy model**:
 +
 [.bordershadow]
 image::04/select-deploy-model.png[]

content/modules/ROOT/pages/06-01-potential-imp-ref.adoc

@@ -11,8 +11,8 @@ But this section of the lab is not meant for Improvements and Refinements **to t
 
 What we have shown in this lab is a very rough prototype, put together very quickly, in order to demonstrate:
 
-* which improvements could be done
-* how long it would take to do them
+* Which improvements could be done
+* How long it would take to do them
 
 In such a situation, it is common to go fast and make short-term decisions since there is no guarantee that this will become a real project.
 
@@ -35,16 +35,16 @@ If you want to read what **we** thought could be improved, read below! (response
 ====
 
 * We could have something that analyzes the images and checks for discrepancies with the customer data, such as:
-** not the same make or color car as what is on file
-** mismatch in license plate, if visible in the picture
+** Not the same make or color car as what is on file.
+** Mismatch in license plate, if visible in the picture.
 * We've only scratched the surface with gitops and pipelines here
 ** There was no performance testing done. If too many users connect at the same time, it might overwhelm either the app, the database, the LLM, etc...
 * Currently, most simple changes would probably end up breaking the application. And the person who, for example decides to change Mistral7B for Flan-T5-Small would not necessarily realize that.
-** It would be critical to have multiple instances (Dev/Test/UAT/Prod) of the application
+** It would be critical to have multiple instances (Dev/Test/UAT/Prod) of the application.
 ** It would also be required to have integration pipelines run in these environments to confirm that changes made do not break the overall application.
 * We could ask the LLM to start writing a response to the customer.
 ** It could be just to ask for missing details.
-** or it could be to let them know whether the claim is accepted or denied
+** or it could be to let them know whether the claim is accepted or denied.
 * However, to do this, the LLM would have to be aware of the policies that the insurance company uses to make those determinations.
 ** This could be an interesting use-case for the https://research.ibm.com/blog/retrieval-augmented-generation-RAG[RAG,window=_blank] approach.
 

content/modules/ROOT/pages/06-02-applicability-other.adoc

@@ -20,17 +20,17 @@ So let's take some examples, and discuss what we **could** do using the same tec
 == Use cases
 
 1. Healthcare
-   - medical imaging analysis
-   - patient record summarization
+   - Medical imaging analysis
+   - Patient record summarization
 
 2. Automotive
    - Object recognition for autonomous vehicles
 
 3. Agriculture
-   - crop disease analysis
+   - Crop disease analysis
 
 4. Finance
-   - loan application paperwork analysis
+   - Loan application paperwork analysis
 
 5. Manufacturing
    - Quality control through visual inspection of products