Taxonomy Versioning for Consistent ML Data Labeling
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Solution Overview
Problem
Managing and tracking multiple versions of taxonomies in data labeling systems for machine learning models is challenging due to their dynamic nature and the need for consistency across different applications, which affects the quality and throughput of data labeling processes.
Innovation Solution
Implementing taxonomy management systems that allow for multiple versions of taxonomies, enabling automatic creation, updating, and tracking of labels and attributes, and supporting chained and continuous learning applications to improve data labeling efficiency and quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple versions of taxonomies are managed manually, then flexibility in updating labels and attributes is improved, but tracking and consistency across applications deteriorates
Solution Approach 1:
The taxonomy is divided into discrete versioned units, where each version represents a specific snapshot of labels and attributes. This segmentation allows independent management and tracking of different taxonomy versions while maintaining consistency through version references in various applications.
Solution Approach 2:
The system implements automated feedback mechanisms that track taxonomy version usage across multiple applications. When taxonomy versions are updated, the system automatically notifies and updates dependent applications, ensuring consistency and providing feedback loops for maintenance.
2Manufacturing precision
If taxonomy versions are updated frequently to improve data quality, then labeling quality is improved, but system complexity and tracking difficulty worsen
Solution Approach 1:
Instead of modifying the original taxonomy in place, the system creates copies as new versions. Each version is a complete snapshot that can be independently managed and referenced, simplifying tracking while enabling frequent updates for improved labeling quality.
Solution Approach 2:
The system performs preliminary versioning and validation before applying taxonomy updates to production. This allows quality improvements to be prepared and tested in advance, reducing the complexity of live updates while maintaining high labeling standards.
3Productivity
If automated taxonomy versioning is implemented, then consistency and efficiency are improved, but initial system setup and configuration complexity worsen
Solution Approach 1:
The automated versioning system is designed to be universally applicable across different taxonomy types and applications. By creating a multi-functional framework that handles various labeling scenarios through common versioning mechanisms, the initial configuration complexity is amortized across multiple use cases, improving overall productivity.
Data Source
AI summary
Taxonomy management may be performed for generating and executing data labeling jobs. Data labeling taxonomy versions are managed and reused for data labeling applications generated and executed by a data labeling system. Instead of building labeling jobs through the use of manually managed configuration files that provide a taxonomy, a single taxonomy can be registered, updated, and used multiple times for various different types of labeling scenarios, including chaining jobs and continual learning loops.