Kantar Worldpanel fine-tunes GenAI models on Databricks to generate market-insight training data faster
Kantar Worldpanel used the Databricks Data + AI Platform and MLflow to experiment with Llama, Mistral, GPT-4 and GPT-3.5 for a proof of concept linking receipt descriptions to product barcode names. GPT-4 produced the most accurate outputs (94%), which the team used to automatically generate a training dataset of about 120,000 receipt-to-barcode pairs in a couple of hours to fine-tune a smaller production model.
Overview
Kantar Worldpanel used the Databricks Data + AI Platform and MLflow to experiment with Llama, Mistral, GPT-4 and GPT-3.5 for a proof of concept linking receipt descriptions to product barcode names. GPT-4 produced the most accurate outputs (94%), which the team used to automatically generate a training dataset of about 120,000 receipt-to-barcode pairs in a couple of hours to fine-tune a smaller production model.
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Inspect the highlighted sourceThe challenge
Kantar Worldpanel's legacy systems were inflexible, resource-intensive to maintain, and required specialized, outdated programming skillsets, limiting data democratization and experimentation with new AI-driven use cases.
The solution
Kantar Worldpanel used the Databricks Data + AI Platform and MLflow to manage the ML lifecycle, experimenting with Llama, Mistral, GPT-4 and GPT-3.5 to fine-tune a model linking receipt descriptions to product barcode names, downloading models via Databricks Marketplace, exploring Databricks AI Search for description comparisons, and using Unity Catalog to govern data sharing across teams.
Reported business value
Kantar Worldpanel automatically generated a training dataset of about 120,000 receipt-to-barcode description pairs at 94% accuracy in just a couple of hours, letting manual coding teams focus on discrepant results and freeing engineering resources for core development, while streamlining data scientists' workflows.
Sources
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