Ensemble of Agents for Rapid Data Annotation
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Solution Overview
Problem
The traditional approach to implementing machine learning (ML) models is hindered by the time-consuming process of collecting, annotating, and augmenting data, which can take several months and often results in delayed deployment of ML solutions.
Innovation Solution
The use of an ensemble of agents (EoAs) system that includes Bright pool agents and Dark pool agents, where agents can be Foundation Models, human annotators, or ML models, to rapidly annotate data. This system performs inference operations, combines outputs, and performs ground-truthing to generate labeled outputs, thereby speeding up the data annotation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data collection and annotation methods are used, then data quality and accuracy are improved, but the time required for deployment increases significantly
Solution Approach 1:
The system segments the annotation task by dividing the dataset into multiple partitions and assigning different agents (Foundation Models, human annotators, ML models) to different segments. This parallel processing approach maintains annotation quality through diverse agent contributions while dramatically reducing overall deployment time by eliminating sequential bottlenecks.
Solution Approach 2:
The system merges multiple types of agents (Foundation Models, human annotators, and ML models) into a unified ensemble annotation system. This combination leverages the strengths of each agent type to produce high-quality annotations more quickly than any single agent could achieve alone, resolving the contradiction between accuracy and speed.
2Reliability
If manual annotation processes are used, then annotation quality is maintained, but productivity and output speed decrease
Solution Approach 1:
The system creates a universal annotation framework where multiple agent types (Foundation Models, human annotators, ML models) can perform the same annotation function simultaneously. This multi-functional approach maintains reliability by allowing quality verification across different agent types while increasing productivity through parallel processing of large volumes of data.
Solution Approach 2:
The ensemble annotation system enables self-service annotation by allowing Foundation Models and ML models to automatically generate annotations without requiring constant human intervention. This automation maintains quality through the ensemble verification process while dramatically increasing annotation throughput and productivity.
3Manufacturing precision
If comprehensive data augmentation is performed, then model training quality is improved, but the time and computational resources required increase
Solution Approach 1:
The system performs preliminary data augmentation and annotation using the ensemble of agents before model training begins. By pre-processing and pre-annotating data partitions in advance, the system ensures high model training quality is achieved without extending the actual training timeline, as the augmentation work is completed beforehand through parallel agent processing.
Data Source
AI summary
Systems and Methods are described herein for rapid data annotation for ML. Aspects comprise a method for inferencing with an ensemble of agents (“EoAs”), comprising receiving data for processing by one or more agents of the EoAs; selecting a plurality of Bright pool agents from the EoAs; performing a first inference operation with each Bright pool agent of plurality of Bright pool agents based on the received data to generate a plurality of intermediate outputs; performing ground-truthing on one or more of an intermediate outputs and a final output to generate one or more labeled outputs; and storing the labeled outputs in a data repository.