AT&T AutoClassify: Unified Multi-Head Binary Classification From Unlabeled Text
Overview
Experience | In Person |
---|---|
Type | Breakout |
Track | Artificial Intelligence |
Industry | Enterprise Technology, Professional Services |
Technologies | MLFlow, AI/BI, PyTorch |
Skill Level | Intermediate |
Duration | 40 min |
We present AT&T AutoClassify, built jointly between AT&T's Chief Data Office (CDO) and Databricks professional services, a novel end-to-end system for automatic multi-head binary classifications from unlabeled text data. Our approach automates the challenge of creating labeled datasets and training multi-head binary classifiers with minimal human intervention.
Starting only from a corpus of unlabeled text and a list of desired labels, AT&T AutoClassify leverages advanced natural language processing techniques to automatically mine relevant examples from raw text, fine-tune embedding models and train individual classifier heads for multiple true/false labels. This solution can reduce LLM classification costs by 1,000x, making it an efficient solution in operational costs.
The end result is a highly optimized and low-cost model servable in Databricks capable of taking raw text and producing multiple binary classifications. An example use case using call transcripts will be examined.
Session Speakers
Colton Peltier
/Staff Data Scientist
Databricks
Hien Lam
/Senior Data Scientist
AT&T