Vannevar Labs fine-tunes multilingual sentiment analysis model in 2 weeks with Databricks Mosaic AI, reducing latency by 75%
Vannevar Labs needed accurate multilingual sentiment analysis for defense intelligence work, but GPT-4 with prompt engineering achieved only 65% accuracy, was too expensive, and struggled with lower-resourced languages like Tagalog. GPU resource shortages and infrastructure management complexity also blocked the team from fine-tuning their own model.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Ingest data from public sources
MCLI's data ingestion capabilities allow seamless, secure connection to Vannevar's datasets aggregated from multiple public sources.
The fine-tuned model achieved an F1 score of 76%, reduced latency by 75%, and was deployed within 2 weeks, enabling Vannevar Labs to process significantly more data more efficiently at lower cost.
What failed first
GPT-4 with prompt engineering failed to meet accuracy requirements for multilingual sentiment classification, achieving only 65% accuracy while being cost-prohibitive and inadequate for lower-resourced languages.