Merge branch 'main' into PLAT-227083/Dataflow-schedule-behaviour-not-stated-in-documentation

Author: Linh Nguyen

help/accessibility/features.md

@@ -18,6 +18,7 @@ Users with disabilities frequently rely on hardware and software, known as assis
 Experience Platform strives to support full keyboard accessibility.
 
 The following navigational elements facilitate accessibility: 
+
 * The Tab key moves between UI elements, sections, and menu groups.
 * Arrow keys move within menu groups to set focus to individual active elements. 
 * Shift + Tab moves backwards through the tab order. 

help/data-prep/calculated-fields.md renamed to help/data-prep/calculated-fields.draft

@@ -242,7 +242,7 @@ For information on the object copy feature, see the section [below](#object-copy
 
 | Function | Description | Parameters | Syntax | Expression | Sample output |
 | -------- | ----------- | ---------- | -------| ---------- | ------------- |
-| json_to_object | Deserialize JSON content from the given string. | <ul><li>STRING: **Required** The JSON string to be deserialized.</li></ul> | json_to_object​(STRING) | json_to_object​({"info":{"firstName":"John","lastName": "Doe"}}) | An object representing the JSON. |
+| json_to_object | Deserialize JSON content from the given string. | <ul><li>STRING: **Required** The JSON string to be deserialized.</li></ul> | json_to_object​(STRING) | `json_to_object​({"info":{"firstName":"John","lastName": "Doe"}})` | An object representing the JSON. |
 
 {style="table-layout:auto"}
 

help/data-prep/functions.md

@@ -34,10 +34,11 @@ The following workflow takes place when a feature pipeline is run:
 ## Getting started
 
 To run a recipe in any organization, the following is required:
--  An input dataset.
--  The Schema of the dataset.
--  A transformed schema and an empty dataset based on that  schema. 
--  An output schema and an empty dataset based on that schema.
+
+- An input dataset.
+- The Schema of the dataset.
+- A transformed schema and an empty dataset based on that  schema. 
+- An output schema and an empty dataset based on that schema.
 
 All of the above datasets need to be uploaded to the [!DNL Experience Platform] UI. To set this up, use the Adobe-provided [bootstrap script](https://github.com/adobe/experience-platform-dsw-reference/tree/master/bootstrap).
 

help/data-science-workspace/authoring/feature-pipeline.md

@@ -27,6 +27,7 @@ Today's enterprise puts a high priority on mining big data for predictions and i
 As important as it is, getting from data to insights can come at a high cost. It typically requires skilled data scientists who conduct intensive and time-consuming data research to develop machine-learning models, or recipes, which power intelligent services. The process is lengthy, the technology is complex, and skilled data scientists can be hard to find.  
 
 With [!DNL Data Science Workspace], Adobe Experience Platform allows you to bring experience-focused AI across the enterprise, streamlining and accelerating data-to-insights-to-code with:
+
 - A machine learning framework and runtime
 - Integrated access to your data stored in Adobe Experience Platform
 - A unified data schema built on [!DNL Experience Data Model] (XDM)

help/data-science-workspace/home.md

@@ -529,7 +529,9 @@ df1.show(10)
 You can auto generate the above example in JupyterLab buy using the following method:
 
 Select the Data icon tab (highlighted below) in the left-navigation of JupyterLab. The **[!UICONTROL Datasets]** and **[!UICONTROL Schemas]** directories appear. Select **[!UICONTROL Datasets]** and right-click, then select the **[!UICONTROL Explore Data in Notebook]** option from the dropdown menu on the dataset you wish to use. An executable code entry appears at the bottom of your notebook. 
+
 And 
+
 - Use **[!UICONTROL Explore Data in Notebook]** to generate a read cell.
 - Use **[!UICONTROL Write Data in Notebook]** to generate a write cell.
 

help/data-science-workspace/jupyterlab/access-notebook-data.md

@@ -114,6 +114,7 @@ If you restart your kernel and run all the cells again, you should get the same
 ### Explore your data
 
 Now that we can access your data, let's focus on the data itself by using statistics and visualization. The dataset that we are using is a retail dataset which gives miscellaneous information about 45 different stores on a given day. Some characteristics for a given `date` and `store` include the following:
+
 - `storeType`
 - `weeklySales`
 - `storeSize`
@@ -155,6 +156,7 @@ This means 22 stores are of `storeType` `A`, 17 are `storeType` `B`, and 6 are `
 #### Data visualization
 
 Now that we know our data frame values, we want to supplement this with visualizations to make things clearer and easier to identify patterns. Graphs are also useful when conveying results to an audience. Some [!DNL Python] libraries which are useful for visualization include:
+
 - [Matplotlib](https://matplotlib.org/)
 - [pandas](https://pandas.pydata.org/)
 - [seaborn](https://seaborn.pydata.org/)
@@ -195,6 +197,7 @@ Notice the diagonal of 1's down the center. This shows that when comparing a var
 ## Next steps
 
 This tutorial went over how to create a new Jupyter Notebook in the Data Science Workspace and how to access data externally as well as from [!DNL Adobe Experience Platform]. Specifically, we went over the following steps:
+
 - Create a new Jupyter Notebook
 - Access datasets and schemas
 - Explore datasets 

help/data-science-workspace/jupyterlab/analyze-your-data.md

@@ -133,6 +133,7 @@ For an in-depth tutorial on using the `platform_sdk` data loader, please visit t
 ### External sources {#external-sources}
 
 This section shows you how to import a JSON or CSV file to a pandas object. Official documentation from the pandas library can be found here:
+
 - [read_csv](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html)
 - [read_json](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_json.html)
 
@@ -172,6 +173,7 @@ def load(config_properties):
 >[!NOTE]
 >
 >As mentioned in the [Configuration File section](#configuration-files), the following configuration parameters are set for you when you access data from Experience Platform using `client_context = get_client_context(config_properties)`:
+>
 > - `ML_FRAMEWORK_IMS_USER_CLIENT_ID` 
 > - `ML_FRAMEWORK_IMS_TOKEN` 
 > - `ML_FRAMEWORK_IMS_ML_TOKEN` 

help/data-science-workspace/jupyterlab/create-a-model.md

@@ -258,6 +258,7 @@ For a list of supported packages in Python, R, and PySpark, copy and paste `!con
 ![example](../images/jupyterlab/user-guide/libraries.PNG)
 
 In addition, the following dependencies are used but not listed:
+
 * CUDA 11.2
 * CUDNN 8.1
 

help/data-science-workspace/jupyterlab/overview.md

@@ -20,6 +20,7 @@ This tutorial provides you with the prerequisites and assets required for all ot
 ## Getting started
 
 Before starting this tutorial, you must have the following prerequisites:
+
 -   Access to [!DNL Adobe Experience Platform]. If you do not have access to an organization in [!DNL Experience Platform], please speak to your system administrator before proceeding.
 -   Authorization to make [!DNL Experience Platform] API calls. Complete the [Authenticate and access Adobe Experience Platform APIs](https://www.adobe.com/go/platform-api-authentication-en) tutorial to obtain the following values in order to successful complete this tutorial:
     -   Authorization: `{ACCESS_TOKEN}`
@@ -111,6 +112,7 @@ for more information.
 You have also successfully ingested Retail Sales sample data into [!DNL Experience Platform] using the provided bootstrap script.
 
 To continue working with the ingested data:
+
 - [Analyze your data using Jupyter Notebooks](../jupyterlab/analyze-your-data.md)
     - Use Jupyter Notebooks in Data Science Workspace to access, explore, visualize, and understand your data.
 - [Package source files into a Recipe](./package-source-files-recipe.md)