PR for WMS 7401 and WMS 520 (#57)

* OML and OML Fundamentals workshop updated

* OML Fun on ADB updated

* Edits to OML Funda and OML Nnotebooks lab

* Removed "Early Adopter" from lab 1 title

Removed link to Conda lab.

* Data Studio updates

The new Development and Related Services tabs updated

* Data Studio update

Database Action UI update - added new screenshot to show Development tab UI in DB Actions.

* Database Actions update

* Database Actions related updates

* Changed OML Notebooks to OML Notebooks Classic

* Screenshots updated.

OML Notebooks EA --> OML Notebooks
OML Notebooks --> OML Notebooks Classic

* automl.md updated

* Date updated

* OML4Py updated. OML Notebooks EA --> OML Notebooks

* Screenshots updated to remove EA (Early Adopter)

* Changes to screenshots.

OML Notebooks EA --> OML Notebooks

oml-fun-on-adb/automl/automl.md

@@ -325,4 +325,4 @@ You may now **proceed to the next lab**.
 
 * **Author** - Moitreyee Hazarika, Principal User Assistance Developer, Database User Assistance Development
 * **Contributors** -  Mark Hornick, Senior Director, Data Science and Machine Learning; Marcos Arancibia, Senior Principal Product Manager, Machine Learning; Sherry LaMonica, Consulting Member of Tech Staff, Machine Learning
-* **Last Updated By/Date** - Moitreyee Hazarika, October 2023
+* **Last Updated By/Date** - Moitreyee Hazarika, June 2024

oml-fun-on-adb/oml-notebooks/oml-notebooks.md

@@ -1,21 +1,21 @@
-# Introduction to Oracle Machine Learning Notebooks
+# Introduction to Oracle Machine Learning Notebooks Classic
 
 ## Introduction
 
-This lab walks you through the steps to sign into Oracle Machine Learning, create an Oracle Machine Learning (OML) notebook from scratch, create an OML notebook based on the example template notebooks, and create jobs to schedule notebooks to run at specific day and time.
+This lab walks you through the steps to sign into Oracle Machine Learning, create an Oracle Machine Learning (OML) Notebook Classic from scratch, create an OML Notebook Classic based on the example template notebooks, and create jobs to schedule notebooks to run at specific day and time.
 
 Estimated Time: 15 minutes
 
-### About Oracle Machine Learning Notebooks
+### About Oracle Machine Learning Notebooks Classic
 
-Oracle Machine Learning Notebooks is a collaborative user interface supporting data scientists, analysts, developers, and DBAs. You can work with SQL, PL/SQL, Python, and R in the same notebook—using the most appropriate language for the problem at hand. You can also view notebook changes by team members in real time, interactively. Data science team members can explicitly share notebooks and version notebooks as well as schedule notebooks to run at a set time or a repeating schedule. By virtue of being included in Oracle Autonomous Database, machine learning functionality is automatically provisioned and managed. Through Oracle Machine Learning Notebooks, you have access to the in-database algorithms and analytics functions to explore and prepare data, build and evaluate models, score data, and deploy solutions.
+Oracle Machine Learning Notebooks Classic is a collaborative user interface supporting data scientists, analysts, developers, and DBAs. You can work with SQL, PL/SQL, Python, and R in the same notebook—using the most appropriate language for the problem at hand. You can also view notebook changes by team members in real time, interactively. Data science team members can explicitly share notebooks and version notebooks as well as schedule notebooks to run at a set time or a repeating schedule. By virtue of being included in Oracle Autonomous Database, machine learning functionality is automatically provisioned and managed. Through Oracle Machine Learning Notebooks, you have access to the in-database algorithms and analytics functions to explore and prepare data, build and evaluate models, score data, and deploy solutions.
 
 ### Objectives
 
 In this lab, you will learn how to:
 * Sign into Oracle Machine Learning User Interface
-* Create an Oracle Machine Learning notebook from scratch
-* Create a notebook based on an example template
+* Create an Oracle Machine Learning Notebook Classic from scratch
+* Create a Notebook Classic based on an example template
 * Check and update the interpreter binding settings for a notebook
 * Schedule a notebook to run at a specific time using the Jobs interface
 
@@ -27,7 +27,7 @@ This lab assumes you have:
 
 ## Task 1: Sign into Oracle Machine Learning User Interface
 
-A notebook is a web-based interface for data analysis, data discovery, data visualization, and collaboration. You create and run notebooks using Oracle Machine Learning Notebooks, which is accessed through the Oracle Machine Learning user interface on Oracle Autonomous Database.
+A Notebook Classic is a web-based interface for data analysis, data discovery, data visualization, and collaboration. You create and run notebooks using Oracle Machine Learning Notebooks, which is accessed through the Oracle Machine Learning user interface on Oracle Autonomous Database.
 
 <if type="freetier">
 
@@ -79,20 +79,20 @@ A notebook is a web-based interface for data analysis, data discovery, data visu
 </if>
 This completes the task of accessing Oracle Machine Learning user interface.
 
-## Task 2: Create a Notebook and define paragraphs using the md, SQL, PL/SQL, Python, and R interpreters
+## Task 2: Create a Notebook Classic and define paragraphs using the md, SQL, PL/SQL, Python, and R interpreters
 
 To create a notebook:
 
-1. On the Oracle Machine Learning home page, click **Notebooks**. The Notebooks page opens. Alternatively, you can click the Cloud menu icon ![hamburger icon](images/hamburger.png) on the top left corner of the home page to open the left navigation menu. Click **Notebooks**.
+1. On the Oracle Machine Learning user interface, click the Cloud menu icon ![hamburger icon](images/hamburger.png) on the top left corner of the home page to open the left navigation menu. Expand Projects and then click **Notebooks Classic**.
 
-	![Notebooks option in hamburger](images/hamburger-notebooks.png)
->> Note: Observe the new entry **Notebooks EA** on the left navigation menu. To learn how to use Oracle Machine Learning Notebooks Early Adopter - an enhanced web-based notebook platform, refer to [Get Started with Notebooks Early Adopter](https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/get-started-notebooks-ea-data-analysis-and-data-visualization.html#GUID-B309C607-2232-43E2-B4A1-655DB295B90B).
+	![Notebooks option in hamburger](images/oml-classic-leftnav.png)
+>> Note: Observe the entry **Notebooks** on the left navigation menu. To learn how to use Oracle Machine Learning Notebooks - an enhanced web-based notebook platform, refer to [Get Started with Notebooks](https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/get-started-notebooks-ea-data-analysis-and-data-visualization.html#GUID-B309C607-2232-43E2-B4A1-655DB295B90B).
 
-2. On the Notebooks page, click **Create**. The Create Notebook dialog opens.
+2. On the Notebooks Classic page, click **Create**. The Create Notebook dialog opens.
 
-	<if type="livelabs">![Create Notebook dialog](images/create-notebook.png) </if>
+	<if type="livelabs"> ![Create Notebook dialog](images/create-notebook-classic.png)</if>
 
-	<if type="freetier"> ![Create Notebook dialog](images/create-notebook-freetier.png) </if>
+	<if type="freetier"> ![Create Notebook dialog](images/create-notebook-classic-freetier.png)</if>
 
 3. In the Name field, enter **Test Notebook**.
 
@@ -337,8 +337,8 @@ These steps demonstrate how to create the Time Series notebook based on the Exam
 
 7. Click the Cloud menu icon ![hamburger icon](images/hamburger.png) on the top left corner of the page to open the left navigation menu. Click **Examples** under Templates to open the Examples page. If you choose to go to the home page, then click **Home** on the left navigation pane, and then click **Examples**.
 
-	<if type="livelabs"> ![Go to Examples](images/goto-examples.png) </if>
-	<if type="freetier"> ![Go to Examples](images/goto-examples-freetier.png) </if>
+	<if type="livelabs"> ![Go to Examples](images/notebook-listed.png) </if>
+	<if type="freetier"> ![Go to Examples](images/notebooks-3.png) </if>
 
 8. Navigate to the **OML4SQL Time Series ESM** example template notebook. You may type ESM in the search box on the upper right corner of the page to get a list of the ESM-related notebooks. Click the grey box around the notebook. This highlights the notebook and enables the **Create Notebook** button. Click **Create Notebook**.
 
@@ -478,4 +478,4 @@ This completes the task of creating a job. You may now **proceed to the next lab
 
 * **Author** -  Moitreyee Hazarika, Principal User Assistance Developer, Database User Assistance Development
 * **Contributors** -   Mark Hornick, Senior Director, Data Science and Machine Learning; Marcos Arancibia Coddou, Product Manager, Oracle Data Science; Sherry LaMonica, Consulting Member of Tech Staff, Machine Learning
-* **Last Updated By/Date** - Moitreyee Hazarika, May 2024
+* **Last Updated By/Date** - Moitreyee Hazarika, June 2024

oml-fun-on-adb/workshops/freetier/manifest.json

@@ -27,12 +27,12 @@
             "filename": "../../get-started-livelabs/get-started-livelabs.md"
         },
         {
-            "title": "Lab 1: Introduction to Oracle Machine Learning Notebook",
+            "title": "Lab 1: Introduction to Oracle Machine Learning Notebooks",
             "type": "freetier",
             "filename": "/common/building-blocks/blocks/oml/oml-notebooks-ea/oml-notebooks-ea.md"
         },
         {
-            "title": "Lab 2: Introduction to Oracle Machine Learning Classic Notebooks",
+            "title": "Lab 2: Introduction to Oracle Machine Learning Notebooks Classic",
             "type": "freetier",
             "description": "Labs that follow the introduction are numbered, starting with Lab 1",
             "filename": "../../oml-notebooks/oml-notebooks.md"

oml4py-adb/automl-ui/automl-ui.md

@@ -41,7 +41,7 @@ To access AutoML UI, you must sign into the Oracle Machine Learning User Interfa
 
 1. Sign into Oracle Machine Learning user interface (following Lab1, Task 1 instructions).
 
-2. On the Oracle Machine Learning home page, either click on the **AutoML** icon in the Quick Actions section or click the "hamburger" menu (the three lines) on the upper left of the screen, and then select **AutoML Experiments**.
+2. On the Oracle Machine Learning home page, either click on the **AutoML** icon in the Quick Actions section or click on the Cloud menu icon ![](images/cloud-menu-icon.png) on the upper left of the page to open the left navigation pane, and then select **AutoML Experiments**.
 
 	![home page](images/homepage-automl.png " ")
 
@@ -186,7 +186,7 @@ When you deploy a model using the Oracle Machine Learning AutoML UI, you create
 
 > **Note:** Through Oracle Machine Learning AutoML UI, you can deploy in-database models only, not ONNX-format (third-party) models.
 
-1. To deploy a model, if you are not already in an AutoML Experiments page, go to one by clicking the hamburger icon on the top left corner of the OML page and click **AutoML Experiments** on the left navigation menu.  
+1. To deploy a model, if you are not already in an AutoML Experiments page, open the left navigation menu by clicking the on the Cloud menu icon ![](images/cloud-menu-icon.png) on the upper left corner of the page, and click **AutoML Experiments**.  
 
 	![home page](images/hamburger-automl.png " ")
 
@@ -221,7 +221,7 @@ This completes the task of deploying the top model to Oracle Machine Learning Se
 
 The deployed models are listed under **Deployments** on the Models page.  To view the metadata of the deployed model **pred_affinity**, follow the steps below.
 
-1. To go to Deployments, click the three-line (hamburger) icon ![hamburger icon](images/hamburger.png) to open the left navigation menu. Click  **Models**. Alternatively, you can click **Models** on the Oracle Machine Learning home page.
+1. To go to Deployments, click the Cloud menu icon ![Cloud menu icon](images/hamburger.png) to open the left navigation menu. Click  **Models**. Alternatively, you can click **Models** on the Oracle Machine Learning home page.
 
   ![Models](images/models-option.png " ")
 
@@ -277,12 +277,11 @@ This completes the task of creating the notebook **Customer 360 RF (1)** based o
 
 To view the generated notebook Customer 360:
 
-1. Click the hamburger icon ![hamburger icon](images/hamburger.png) on the top left corner of the page to open the left navigation menu. Click **Notebooks** (please note, this is not the Notebooks EA).
+1. Click the Cloud menu icon ![Cloud menu icon](images/hamburger.png) on the top left corner of the page to open the left navigation menu. Click **Notebooks.**
 
 	![Notebooks](images/hamburger-notebooks1.png)
 
-	> **Note:** The Automatic creation of a Notebook from AutoML UI is built into the Notebooks version, and not the Notebooks EA version at this time.
-
+
 2. The Notebooks page opens with any existing notebooks listed in it. Click the **Customer 360 RF (1)** notebook to open it.
 
     ![Generated Notebook](images/notebooks-listed-final.png " ")
@@ -291,25 +290,38 @@ To view the generated notebook Customer 360:
 
 	![Run all](images/run-all.png " ")
 
-    In the Run All confirmation dialog, click **OK**.  
+    In the Run All paragraphs confirmation dialog, click **Confirm**.  
 
 7. Scroll down to view all the paragraphs in the notebook:
 
 * The paragraph titled _Oracle Machine Learning AutoML UI - Experiment - Generated Notebook_ contains the experiment metadata.
 
 	![Experiment metadata](images/experiment-metadata.png " ")
 
-* The paragraph titled _Get proxy object for selected data_ contains the code to get the proxy object for the data used in the experiment, which is the Customers360 table here. The paragraph titled _Prepare Training Data_ contains the code to prepare the training data.
+* The paragraph titled _Get proxy object for selected data_ contains the code to get the proxy object for the data used in the experiment, which is the Customers360 table here. 
+
+	![Generated Notebook- Proxy object for data](images/generated-proxy-prep.png " ")
+
+* The paragraph titled _Prepare Training Data_ contains the code to prepare the training data.
+
+	![Generated Notebook - Prepare Training Data](images/prepare-training-data.png " ")
+
+* The paragraph titled _Build Model_ contains the code to build the model. In this lab, it is a Random Forest model. The settings (hyperparameters) optimized by AutoML to create the model are shown here, so that you are able to reproduce the model using OML4Py as shown in the ``rf_settings`` variable. 
 
-	![Generated Notebook](images/generated-proxy-prep.png " ")
+	![Generated Notebook](images/build-model.png " ")
 
-* The paragraph titled _Build Model_ contains the code to build the model. In this lab, it is a Random Forest model. The settings (hyperparameters) optimized by AutoML to create the model are shown here, so that you are able to reproduce the model using OML4Py as shown in the ``rf_settings`` variable. The paragraph titled _Show Model Details_ contains the code to view the model details.
+* The paragraph titled _Show Model Details_ contains the code to view the model details.
+
+	![Generated Notebook](images/show-model-details.png " ")
+
+* The paragraph titled _Data for Scoring_ and _Score data_ contains the code to score the data. 
 
 	![Generated Notebook](images/generated-build-show.png " ")
 
-* The paragraph titled _Data for Scoring_ contains the code to score the data, and the paragraph _Show model quality metric_ contains the code to view the model quality metric.
 
-	![Generated Notebook](images/generated-score-metric.png " ")
+* The paragraph _Show model quality metric_ contains the code to view the model quality metric. 
+
+	![Generated Notebook](images/model-quality-metric.png " ")
 
 This completes the task of creating a notebook based on a model and viewing the paragraphs contained in it.
 
@@ -329,4 +341,4 @@ This completes the task of creating a notebook based on a model and viewing the
 
 * **Author** - Marcos Arancibia, Senior Principal Product Manager, Machine Learning; Moitreyee Hazarika, Principal User Assistance Developer, Database User Assistance Development
 * **Contributors** -  Mark Hornick, Senior Director, Data Science and Machine Learning; Sherry LaMonica, Consulting Member of Tech Staff, Machine Learning
-* **Last Updated By/Date** - Marcos Arancibia, August 2023
+* **Last Updated By/Date** - Marcos Arancibia, August 2023, Moitreyee Hazarika, June 2024

oml4py-adb/automl/automl.md

@@ -25,7 +25,7 @@ In this lab, you will learn how to:
  > **NOTE:** If you have problems with downloading and extracting the ZIP file in Lab 1 Task 2, please 
  <if type="freetier">[**CLICK HERE** to download the "Lab 8 - Use AutoML" notebook DSNB file](<./../notebooks/Lab 8 - Use AutoML.dsnb?download=1>)</if><if type="livelabs">[**CLICK HERE** to download the "Lab 8 - Use AutoML" notebook DSNB file](<./../notebooks/Lab 8 - Use AutoML.dsnb?download=1>)</if><if type="freetier-ocw23">[**CLICK HERE** to download the "Lab Bonus 4 - Use AutoML" notebook DSNB file](<./../notebooks/Lab Bonus 4 - Use AutoML.dsnb?download=1>)</if><if type="livelabs-ocw23">[**CLICK HERE** to download the "Lab Bonus 4 - Use AutoML" notebook DSNB file](<./../notebooks/Lab Bonus 4 - Use AutoML.dsnb?download=1>)</if>. This notebook contains the scripts for this Lab. Save it to your local machine and import it like illustrated in **Lab 1, Task 2, Step 1**.
 
-   Go back to the main Notebooks listing by clicking on the "hamburger" menu (the three lines) on the upper left of the screen, and then select **Notebooks EA**. 
+   Go back to the main Notebooks page by clicking on the Cloud menu icon ![](images/cloud-menu-icon.png) on the upper left of the screen to open the left navigation pane, and then click **Notebooks**. 
 
    ![Go to main Notebooks EA](images/go-back-to-notebooks.png " ")
 
@@ -122,4 +122,4 @@ You can explore additional workshops related to Oracle Machine Learning from the
 ## Acknowledgements
 * **Authors** - Marcos Arancibia, Product Manager, Machine Learning; Jie Liu, Data Scientist; Moitreyee Hazarika, Principal User Assistance Developer
 * **Contributors** -  Mark Hornick, Senior Director, Data Science and Machine Learning; Sherry LaMonica, Principal Member of Tech Staff, Machine Learning
-* **Last Updated By/Date** - Marcos Arancibia, August 2023
+* **Last Updated By/Date** - Marcos Arancibia, August 2023, Moitreyee Hazarika, June 2024

oml4py-adb/data-slct-mnpn/data-slct-mnpn.md

@@ -49,9 +49,9 @@ In this lab, you will learn how to:
   > **NOTE:** If you have problems with downloading and extracting the ZIP file in Lab 1 Task 2, please [**CLICK HERE** to download the "Lab 2 - Abridged OCW23 - Select and manipulate data with the Transparency Layer." notebook DSNB file](<./../notebooks/Lab 2 - Abridged OCW23 - Select and manipulate data with the Transparency Layer.dsnb?download=1>). This notebook contains the scripts for **Lab 2**. Save it to your local machine and import it like illustrated in **Lab 1, Task 2, Step 1**.
  </if>
 
-   Go back to the main Notebooks listing by clicking on the "hamburger" menu (the three lines) on the upper left of the screen, and then select **Notebooks EA**. 
+   Go back to the main Notebooks page by clicking on the Cloud menu icon ![](images/cloud-menu-icon.png) on the upper left of the screen to open the left navigation pane, and then click **Notebooks**. 
 
-   ![Go to main Notebooks EA](images/go-back-to-notebooks.png " ")
+   ![Go to main Notebooks](images/go-back-to-notebooks.png " ")
 
    <if type="freetier">
    Click the **Lab 2** notebook to view it.
@@ -180,4 +180,4 @@ You can now *proceed to the next lab*.
 ## Acknowledgements
 * **Authors** - Marcos Arancibia, Product Manager, Machine Learning; Jie Liu, Data Scientist; Moitreyee Hazarika, Principal User Assistance Developer
 * **Contributors** -  Mark Hornick, Senior Director, Data Science and Machine Learning; Sherry LaMonica, Principal Member of Tech Staff, Machine Learning
-* **Last Updated By/Date** - Marcos Arancibia, August 2023
+* **Last Updated By/Date** - Marcos Arancibia, August 2023, Moitreyee Hazarika, June 2024