MS Azure Machine Learning activity

An activity that creates score models based on data from your AgilePoint NX app that Microsoft Azure Machine Learning can analyze.

Configure the MS Azure Machine Learning activity

To configure the MS Azure Machine Learning activity, do the procedure in this topic.

Examples

Good to Know

To send an email notification, click Advanced > Email Notifications .
For more information, refer to Email Notifications screen (Process Activities).
In most text fields, you can use process data variables as an alternative to literal data values.
You can configure whether this activity waits for other activities before it runs.
For more information, refer to How Do I Configure an Activity to Wait for Other Incoming Activities?
This section shows the configuration settings on the configuration screens for this activity. Other configuration settings apply to system activities. For more information, refer to:
Some information about third-party integrations is outside the scope of the AgilePoint NX Product Documentation. It is the responsibility of the vendors who create and maintain these technologies to provide this information. This includes specific business use cases and examples; explanations for third-party concepts; details about the data models and input and output data formats for third-party technologies; and various types of IDs, URL patterns, connection string formats, or other technical information that is specific to the third-party technologies. For more information, refer to Where Can I Find Information and Examples for Third-Party Integrations?

How to Start

Open Process Builder.
For information about how to open this screen, refer to Process Builder.
In Process Builder, in the Activity Library open the Machine Learning tab.
On the Machine Learning tab, drag the MS Azure Machine Learning activity onto your process.

General Configuration

Specifies the basic settings for the MS Azure Machine Learning activity.

Good to Know

Documentation for this screen is provided for completeness. The fields are mostly self-explanatory.

Fields

Field Name	Definition
Display Name	Function: Specifies the activity name that shows in your process. Accepted Values: One line of text that can have spaces. Default Value: None Accepts Process Data Variables: No Example: This is a common configuration field that is used in many examples. Refer to: Examples - Step-by-step use case examples, information about what types of examples are provided in the AgilePoint NX Product Documentation, and other resources where you can find more examples.
Description	Function: Specifies an optional text description for your activity. Accepted Values: More than one line of text. Default Value: None Accepts Process Data Variables: No

Field Name

Definition

Display Name

Function:

Specifies the activity name that shows in your process.

Accepted Values:

One line of text that can have spaces.

Default Value:

None

Accepts Process Data Variables:

Example:

This is a common configuration field that is used in many examples. Refer to:

Examples - Step-by-step use case examples, information about what types of examples are provided in the AgilePoint NX Product Documentation, and other resources where you can find more examples.

Description

Function:: Specifies an optional text description for your activity.
Accepted Values:: More than one line of text.
Default Value:: None
Accepts Process Data Variables:: No

Machine Learning Configuration

Configures score models with your process, activities, and schema.

Fields

Field Name	Definition
Add Score Model	Function: Creates a score model with a name you specify.
Edit	Function: Changes the selected item.
Delete	Function: Deletes the selected item. Example: Refer to: The videos on this page
MS Azure Machine Learning	Function: Specifies the access token that connects to your Microsoft Azure Machine Learning service. Accepted Values: A list of access tokens configured for your environment. Default Value: None Accepts Process Data Variables: No Example: Refer to: (Example) How to Get the Access Token Credentials from Microsoft Azure Machine Learning
Create	Opens this Screen: Access Token for Microsoft Azure Machine Learning Function of this Screen: Configure an access token to connect to Microsoft Azure Machine Learning. Example: Refer to: (Example) How to Get the Access Token Credentials from Microsoft Azure Machine Learning
Activity	Function: Specifies an activity in your process you want Microsoft Microsoft Azure Machine Learning to use in your score model. Accepted Values: An activity from the list. Default Value: None Accepts Process Data Variables: No
On Event	Function: Specifies the activity's event you want to use in your score model. For a human task activity, you can select events for the activity or the tasks the activity creates. For a system activity, you can select only events for the activity. Accepted Values: Assign Work Item - Microsoft Azure Machine Learning runs when a task is Assigned to a participant. Complete Work Item - Microsoft Azure Machine Learning runs when a task is Completed. Cancel Work Item - Microsoft Azure Machine Learning runs when a task is Cancelled. Enter Activity Instance - Microsoft Azure Machine Learning runs when the activity starts. Leave Activity Instance - Microsoft Azure Machine Learning runs when the the activity ends. Reassign Work Item - Microsoft Azure Machine Learning runs when a task is Reassigned. Work Item Assigned - Microsoft Azure Machine Learning runs when a task is Assigned to a participant. Work Item Overdue - Microsoft Azure Machine Learning runs when a task is Overdue. Default Value: None
Request Mapping	Function: Connects the Microsoft Azure Machine Learning API request parameter to your process schema. This is mandatory only if the request passes data from the process schema to parameters in the Microsoft Azure Machine Learning API. To Open this Field: On the Machine Learning Configuration screen, click the Request tab. Accepted Values: Click the Schema Mapping button to open the Schema Mapper. Use this screen to connect the request to the data model for your process. Default Value: None Example: Refer to: (Example) Schema Mapper
Map Response to AgilePoint Schema	Function: Specifies the connection from the Microsoft Azure Machine Learning API response parameter to your process schema. To Open this Field: On the Machine Learning Configuration screen, click the Response tab. Accepted Values: Click the Schema Mapping button to open the Schema Mapper. Use this screen to connect the response to the data model for your process. Default Value: None Example: Refer to: (Example) Schema Mapper
Store the Response in Custom Attribute	Function: Specifies the process data variable that stores the responses from the Microsoft Azure Machine Learning API method call. To Open this Field: On the Machine Learning Configuration screen, click the Response tab. Accepted Values: A process data variable that accepts an alphanumeric string in JSON format. Default Value: None Accepts Process Data Variables: Yes Example: [{"Results":{"output1":{"type":"table","value":{"ColumnNames":["Age","State","CardType","Scored Labels","Scored Probabilities"],"ColumnTypes":["Int32","String","String","String","Double"],"Values":[["21","CA","visa","High","0.0183113589882851"]]}}}}]