MLPredictionDefinition in Einstein Discovery

Architectural Overview#

The MLPredictionDefinition metadata type represents the operational configuration of an AI/ML predictive model in Salesforce Einstein (including Einstein Discovery and Einstein Prediction Builder). In modern enterprise CRM architectures, organizations use predictive intelligence to forecast deal closure likelihood, predict customer churn, estimate customer lifetime value, and score support ticket escalation risk.

MLPredictionDefinition binds high-level machine learning models to the core Salesforce data model. It defines the parent AI application context, the predictive problem type (BinaryClassification, Regression), operational status (Enabled, Disabled), and the target custom field (pushbackField) where calculated scores and top predictive factors are written back to standard or custom records.

Directory and File Conventions#

MLPredictionDefinition components reside in the mlPredictionDefinitions/ folder in standard Salesforce DX source format.

Key Metadata Fields#

The MLPredictionDefinition XML schema establishes the predictive parameters and destination fields:

Declarative XML and Package Manifest Examples#

The following example shows an active prediction definition scoring opportunity win propensity:

<?xml version="1.0" encoding="UTF-8"?>
<MLPredictionDefinition xmlns="http://soap.sforce.com/2006/04/metadata">
    <developerName>Opportunity_Win_Prediction</developerName>
    <masterLabel>Opportunity Win Propensity</masterLabel>
    <aiApplicationDeveloperName>Sales_AI_Application</aiApplicationDeveloperName>
    <status>Enabled</status>
    <type>BinaryClassification</type>
    <predictionField>IsWon</predictionField>
    <pushbackField>Win_Propensity_Score__c</pushbackField>
</MLPredictionDefinition>

Package Manifest (package.xml)#

Include MLPredictionDefinition in your deployment manifest:

<?xml version="1.0" encoding="UTF-8"?>
<Package xmlns="http://soap.sforce.com/2006/04/metadata">
    <types>
        <members>*</members>
        <name>MLPredictionDefinition</name>
    </types>
    <version>68.0</version>
</Package>

CLI Retrieval and Deployment Commands#

Manage MLPredictionDefinition metadata using the Salesforce CLI:

# Retrieve all Prediction Definitions
sf project retrieve start --metadata MLPredictionDefinition

# Deploy a specific Prediction Definition
sf project deploy start --metadata MLPredictionDefinition:Opportunity_Win_Prediction

# Query existing prediction definitions via Tooling API
sf data query --use-tooling-api --query "SELECT Id, DeveloperName, Status, Type, PushbackField FROM MLPredictionDefinition"

Architect Best Practices and Gotchas#

• Pushback Field Permissions: Verify that the field referenced in pushbackField has Field-Level Security (FLS) configured to allow the Einstein automated process user to write scores without throwing execution faults.
• Parent AI Application Existence: MLPredictionDefinition requires an existing aiApplicationDeveloperName. Ensure the parent AI application is deployed prior to or alongside the prediction definition.
• Model Activation: When migrating to a production org, deploying an Enabled prediction activates automated scoring pipelines. Verify historical training datasets are adequate before enabling live record updates.

Frequently asked questions

What does MLPredictionDefinition manage in Salesforce?

It manages configuration parameters for machine learning predictions, specifying the parent AI application, prediction model type, status, and score pushback fields.

Where is MLPredictionDefinition stored in SFDX format?

It is stored in force-app/main/default/mlPredictionDefinitions/ with the .mlPredictionDefinition-meta.xml suffix.

What are the common prediction types supported?

Supported types include BinaryClassification, MulticlassClassification, Regression, and ScoringSpecificOutcome.

What does the pushbackField property do?

It specifies the target custom field on the record where Einstein writes real-time predicted scores and likelihood metrics.