MLDataDefinition Metadata Type in Einstein Studio

Architectural Overview#

The MLDataDefinition metadata type represents a modeling dataset definition within Salesforce Einstein (specifically Einstein Prediction Builder, Einstein Recommendation Builder, and Einstein Studio). Developing machine learning models in enterprise architectures requires rigorous boundaries around training datasets, excluded features (to prevent model bias or data leakage), and target scoring populations.

By declaring dataset configurations through MLDataDefinition metadata, enterprise architects maintain reproducible machine learning pipelines. Defining dataset filters and predictor fields in Git ensures that when predictive models move from sandbox evaluation to production scoring, the exact underlying training dataset criteria and ethical AI feature guardrails are preserved across environments.

Directory and File Conventions#

MLDataDefinition components reside in the mlDataDefinitions/ folder in the Salesforce DX source format.

Key Metadata Fields#

The MLDataDefinition metadata schema captures entity relationships, feature selection, and filtering criteria:

Declarative XML and Package Manifest Examples#

The following example defines a prediction dataset on the Case object for escalating risk prediction:

<?xml version="1.0" encoding="UTF-8"?>
<MLDataDefinition xmlns="http://soap.sforce.com/2006/04/metadata">
    <developerName>Case_Escalation_Data_Def</developerName>
    <entityDeveloperName>Case</entityDeveloperName>
    <type>Prediction</type>
    <includedFields>Origin</includedFields>
    <includedFields>Priority</includedFields>
    <includedFields>Reason</includedFields>
    <includedFields>Type</includedFields>
    <excludedFields>SuppliedEmail</excludedFields>
    <excludedFields>SuppliedPhone</excludedFields>
</MLDataDefinition>

Package Manifest (package.xml)#

Include MLDataDefinition in the package manifest:

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

CLI Retrieval and Deployment Commands#

Retrieve and deploy MLDataDefinition components using the Salesforce CLI:

# Retrieve all ML Data Definitions
sf project retrieve start --metadata MLDataDefinition

# Deploy a specific ML Data Definition
sf project deploy start --metadata MLDataDefinition:Case_Escalation_Data_Def

# Query ML Data Definitions via Tooling API
sf data query --use-tooling-api --query "SELECT Id, DeveloperName, EntityDeveloperName, Type FROM MLDataDefinition"

Architect Best Practices and Gotchas#

• Preventing Data Leakage: Ensure fields populated only after an outcome occurs (such as ClosedDate or Resolution_Notes__c) are explicitly placed in excludedFields to avoid artificial 100% predictive accuracy during training.
• Entity Immutability: The entityDeveloperName field cannot be modified on an existing MLDataDefinition. If the base entity changes, delete the existing component and deploy a new definition.
• Coordination with Prediction Definitions: MLDataDefinition is frequently paired with MLPredictionDefinition. Deploy dataset definitions before or alongside prediction definitions to prevent unresolved reference errors.

Frequently asked questions

What does MLDataDefinition define in Salesforce Einstein?

It defines the dataset schema, included features, excluded fields, and filter criteria used to train machine learning models in Einstein Prediction Builder.

Where are MLDataDefinition files stored in SFDX format?

They are located in force-app/main/default/mlDataDefinitions/ with the .mlDataDefinition-meta.xml suffix.

What are the supported MLDataDefinition types?

Supported types include Candidate, Interaction, Prediction, and Recipient, determining the role of the dataset in AI models.

Can MLDataDefinition reference custom fields?

Yes, custom fields can be declared in includedFields or excludedFields arrays to control feature selection for the model.