Get Prediction (MS Azure Machine Learning) activity

Configure the Get Prediction activity

Examples

(External) Introducing the AI Control Tower for Azure Machine Learning
(External) Supercharge Your end-to-end Orchestration with AI Control Tower: Real-Time Monitoring and Smart Decision Making
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.

Prerequisites

AgilePoint NX OnDemand (public cloud), or AgilePoint NX PrivateCloud or AgilePoint NX OnPremises v9.0 Software Update 1 or higher.

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 MS Azure Machine Learning tab.
On the MS Azure Machine Learning tab, drag the Get Prediction activity onto your process.

General Configuration

Specifies the basic settings for the Get Prediction activity.

Good to Know

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

Fields

Field Name	Definition
Display Name	Description: Specifies the activity name that shows in your process. Allowed Values: One line of text (a string). Accepted: Letters Numbers Spaces Default Value: None Accepts 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	Description: Specifies an optional text description for your activity. Allowed Values: More than one line of text. Default Value: None Accepts Variables: No

Field Name

Definition

Display Name

Description:

Specifies the activity name that shows in your process.

Allowed Values:

One line of text (a string).

Accepted:

Letters
Numbers
Spaces

Default Value:

None

Accepts 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

Description:: Specifies an optional text description for your activity.
Allowed Values:: More than one line of text.
Default Value:: None
Accepts Variables:: No

Get Prediction > MS Azure Machine Learning tab

Configures the Microsoft Azure Machine Learning model to retrieve information about a prediction.

Fields

Field Name	Definition
Add Model	Function: Creates a model with a name you specify. In this activity, you can create more than one model to configure an Microsoft Azure Machine Learning model to give predictions for an app. Each configuration is stored as a model with a name specified in this field. Limitations: 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?
Clone	Function: Creates a new model that is a copy of the selected model.
Edit	Function: Changes the model name.
Delete	Function: Deletes the selected model.
MS Azure Machine Learning	Description: Specifies the access token that connects to your Microsoft Azure Machine Learning service. Allowed Values: A list of access tokens configured for your environment. Default Value: None Accepts Variables: No Example: Refer to: (Example) How to Get the Access Token Credentials from Microsoft Azure Machine Learning (External) Introducing the AI Control Tower for Azure Machine Learning
MS Azure Machine Learning	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.
Save	Opens this Screen: Saves the configuration for your model and does not close the configuration screen.

Get Prediction > Triggering Event tab

Configures an activity on which to run the Microsoft Azure Machine Learning model.

Fields

Field Name	Definition
Activity	Description: Specifies an activity on which to run the Microsoft Azure Machine Learning model. Allowed Values: An activity from the list. Default Value: None Accepts Variables: No
On Event	Description: Specifies the status for an activity instance where the change occurs. Allowed Values: Assign Work Item - Microsoft Azure Machine Learning runs when the specified task is ready to be 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 instance starts. Leave Activity Instance - Microsoft Azure Machine Learning runs when the activity instance ends. Reassign Work Item - Microsoft Azure Machine Learning runs when a task is Reassigned. Work Item Assigned - Microsoft Azure Machine Learning runs after the specified task is Assigned to a participant. Work Item Overdue - Microsoft Azure Machine Learning runs when a task is Overdue. Default Value: None
Set Trigger Condition	Opens this Screen: Get Prediction > Triggering Event tab > Set Trigger Condition Function of this Screen: Specifies the conditions that cause the Microsoft Azure Machine Learning model to run.

Get Prediction > Triggering Event tab > Set Trigger Condition

Specifies the conditions that cause the Microsoft Azure Machine Learning model to run.

Get Prediction Configuration Triggering Event tab Set Trigger Condition screen — Figure: Get Prediction > Triggering Event tab > Set Trigger Condition screen

Fields

Field Name	Definition
Rule Type	Description: Specifies a rule to execute. Allowed Values: IF - Specifies a rule to execute if a specified condition is true. ELSE IF - Specifies a rule to execute if the previous condition is false. To open this field, click Add New Rule . ELSE - The rule executes if the all the specified conditions are false. Default Value: IF ELSE
Condition	Description: Specifies a name for a rule in your condition. Allowed Values: One line of text (a string). Accepted: Letters Numbers Spaces Default Value: Rule Name 1
Edit	Opens this Screen: Get Prediction > Triggering Event tab > Condition Builder screen Function of this Screen: Creates or changes logical rules. You can put one statement inside another statement to create complex logical expressions.
Delete	Function: Deletes the selected row.

Get Prediction > Triggering Event tab > Condition Builder screen

Creates or changes logical rules. You can put one statement inside another statement to create complex logical expressions.

Get Prediction Configuration Triggering Event tab Condition Builder screen — Figure: Get Prediction > Triggering Event tab > Condition Builder screen

Fields

Field Name

Definition

Condition Name

Description:: Shows the name for your rule.
Allowed Values:: Read only.
Default Value:: Rule Name 1

Preview Preview icon

Function:: Shows the preview of your rule.

Validate Validate icon

Function:: Makes sure the rule is correct.

Rule Variable

Description:

Specifies the value to analyze.

Allowed Values:

One line of text (a string).

Accepted:

Letters
Numbers
Spaces

Default Value:

None

Accepts Variables:

Yes

Example:

Refer to:

Operator

Description:

Lets you select the operators for a logical expression.

Allowed Values:

Data Type	Expression Operators
String	, ==, Starts with, Ends with, Contains, Does Not Contains, !=, Does Not Contain Data, Contains Data
Bool	==
Date Time	, ==, >, >= , <, <=, !=
Number	, ==, >, >=, <, <=, !=

Default Value:

None

Example:

Refer to:

Rule Value

Description:

The value for the data variable.

Allowed Values:

One line of text (a string).

Accepted:

Letters
Numbers
Spaces

Default Value:

None

Accepts Variables:

Yes

Example:

Refer to:

Add Row Add Row icon

Function:: Creates a condition row.

Select Nest Type

Function:

Specifies the use of logical AND and OR operators to nest conditions to use for predictions.

You must set the condition first, before you specify the nest type.

Allowed Values:

And - Specifies the logical AND operator.
Or - Specifies the logical OR operator.

Default Value:

AND

Indent Right Indent Right icon

Function:: Creates the statement that is a condition of a parent statement.

Delete Delete icon

Function:: Deletes the selected row.

Back Back icon

Function:: Saves the rule and goes back to the Create Condition screen.

Get Prediction > Request tab

Specifies how to connect the request data from Microsoft Azure Machine Learning to the AgilePoint process schema.

Get Prediction Configuration Request tab — Figure: Get Prediction > Request tab

Fields

Field Name	Definition
Map Request To App Schema / Enter Sample Request Payload / Read Payload From This Variable	Description: Specifies how to connect the request data from Microsoft Azure Machine Learning to the AgilePoint process schema. Allowed Values: Map Request To App Schema - Connects the Microsoft Azure Machine Learning API request parameter to your process schema. Click Map Request To App Schema to open the Schema Mapper. Use this screen to connect the response to the data model for your process. Enter Sample Request Payload - Specifies a payload to use to connect to the AgilePoint process schema. The payload must be in JSON format. Click the Map Schema button to open the Schema Mapper. Use this screen to connect the payload values to the AgilePoint process schema. Read Payload From This Variable - Specifies a variable that stores the payload. The value of the process data variable must be in a valid JSON format. Default Value: Map Request To App Schema Accepts Variables: Yes Example: Refer to: (External) Introducing the AI Control Tower for Azure Machine Learning Limitations: 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?
Request Mapping	Description: Connects the Microsoft Azure Machine Learning request to the process schema. To Open this Field: Select Map Request To App Schema. Allowed Values: Click the Schema Mapping button to open the Schema Mapper screen. Use this screen to connect the request to the data model for your process. Default Value: None Example: Refer to: (Example) Schema Mapper

Field Name

Definition

Map Request To App Schema / Enter Sample Request Payload / Read Payload From This Variable

Description:

Specifies how to connect the request data from Microsoft Azure Machine Learning to the AgilePoint process schema.

Allowed Values:

Map Request To App Schema - Connects the Microsoft Azure Machine Learning API request parameter to your process schema.
Click Map Request To App Schema to open the Schema Mapper.

Use this screen to connect the response to the data model for your process.
Enter Sample Request Payload - Specifies a payload to use to connect to the AgilePoint process schema.
The payload must be in JSON format.

Click the Map Schema button to open the Schema Mapper.

Use this screen to connect the payload values to the AgilePoint process schema.
Read Payload From This Variable - Specifies a variable that stores the payload. The value of the process data variable must be in a valid JSON format.

Default Value:

Map Request To App Schema

Accepts Variables:

Yes

Example:

Refer to:

(External) Introducing the AI Control Tower for Azure Machine Learning

Limitations:

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?

Request Mapping

Description:

Connects the Microsoft Azure Machine Learning request to the process schema.

To Open this Field:

Select Map Request To App Schema.

Allowed Values:

Click the Schema Mapping Schema Mapping icon

button to open the Schema Mapper screen.

Use this screen to connect the request to the data model for your process.

Default Value:

None

Example:

Refer to:

(Example) Schema Mapper

Get Prediction > Response tab

Specifies where to store the response from Microsoft Azure Machine Learning.

Get Prediction Configuration Response tab — Figure: Get Prediction > Response tab

Fields

Field Name	Definition
Map Response To App Schema / Enter Sample Response Payload / Store Response In This Variable	Description: Specifies where to store the response from Microsoft Azure Machine Learning. Allowed Values: Map Response To App Schema – Select this option if you want to map the information from the Microsoft Azure Machine Learning data model to your process schema. Click the Schema Mapping button to open the Schema Mapper. Use this screen to connect the response to the data model for your process. Enter Sample Response Payload - Specifies a payload to use to map the response to the AgilePoint process schema. The payload must be in JSON format. Click Map Response to open the Schema Mapper. Use this screen to connect the response to the data model for your process. Store Response In This Variable – Select this option if you want to store the information from Microsoft Azure Machine Learning to a variable in the AgilePoint NX app. In the text field, specify a variable that accepts an alphanumeric string in JSON format. Default Value: Map Response To App Schema Accepts Variables: Yes Limitations: 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?

Field Name

Definition

Map Response To App Schema / Enter Sample Response Payload / Store Response In This Variable

Description:

Specifies where to store the response from Microsoft Azure Machine Learning.

Allowed Values:

Map Response To App Schema – Select this option if you want to map the information from the Microsoft Azure Machine Learning data model to your process schema.
Click the Schema Mapping button to open the Schema Mapper.

Use this screen to connect the response to the data model for your process.
Enter Sample Response Payload - Specifies a payload to use to map the response to the AgilePoint process schema.
The payload must be in JSON format.

Click Map Response to open the Schema Mapper.

Use this screen to connect the response to the data model for your process.
Store Response In This Variable – Select this option if you want to store the information from Microsoft Azure Machine Learning to a variable in the AgilePoint NX app.
In the text field, specify a variable that accepts an alphanumeric string in JSON format.

Default Value:

Map Response To App Schema

Accepts Variables:

Yes

Limitations:

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?

Get Prediction > Additional Actions tab

Specifies variables to store the statusof the Microsoft Azure Machine Learning model and to store the error message if an error occurs in Microsoft Azure Machine Learning. It also configures what to do when an error occurs in the AgilePoint NX app.

Get Prediction Configuration Additional Actions tab — Figure: Get Prediction > Additional Actions tab

Fields

Field Name	Definition
Save Status In This Variable (Boolean)	Description: Specifies a variable to store the status as a Boolean value. The value is true if the prediction has no errors. Allowed Values: A variable. Format: Boolean Response Values true false Default Value: None Accepts Variables: Yes Example: Refer to: (External) Supercharge Your end-to-end Orchestration with AI Control Tower: Real-Time Monitoring and Smart Decision Making Limitations: 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?
Save Error Message In This Variable (String)	Description: Specifies a variable to store the error message of the prediction. Allowed Values: A variable. Format: String Accepted: Letters Numbers Spaces Special characters Default Value: None Accepts Variables: Yes Example: Refer to: (External) Supercharge Your end-to-end Orchestration with AI Control Tower: Real-Time Monitoring and Smart Decision Making Limitations: 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?
Suspend Process Instance If Exception Occurs	Description: Specifies whether to move the process forward when an error occurs in the AgilePoint NX app. Allowed Values: Selected - Pauses the process when an error occurs, and shows an error in the log. Deselected - Shows an error in the log when an error occurs, but does not pause the process. Default Value: Selected Example: Refer to: (External) Supercharge Your end-to-end Orchestration with AI Control Tower: Real-Time Monitoring and Smart Decision Making
Enable Debug	Description: Specifies whether to store the debugging information for the AI Control Tower activity in the debug log folder on the AgilePoint Server machine. Allowed Values: Selected - Stores the runtime debugging information for the AI Control Tower activity in a debug log file. A debug log file is created for each process instance. For more information, refer to Where Are the AgilePoint NX OnPremises Logs? Deselected - Does not store the debugging information for the AI Control Tower activity. Default Value: Selected Example: Refer to: (External) Supercharge Your end-to-end Orchestration with AI Control Tower: Real-Time Monitoring and Smart Decision Making