Data Schema Validation

Azure Machine Learning provides robust validation for your data schemas, ensuring data quality and reliability. This page demonstrates a basic validation workflow.

Workflow Overview

The process typically involves:

• Defining a schema with data types and constraints.
• Applying validation rules to detect errors and inconsistencies.
• Generating reports to identify potential issues.

Example Validation Rules (Simplified)

We'll show a basic example – checking for required fields and data types.

Interactive Validation

This section provides a basic illustration of how to access the validation rules through a user interface (via a tool like a JavaScript library or framework).

Refer to the validation rules.html for details.