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The TAO of data: How Databricks is optimizing AI LLM fine-tuning without data labels
New approach flips the script on enterprise AI adoption by using input data you already have for fine-tuning instead of needing labelled data.
Continuous data flywheel: As users interact with the deployed system, new inputs are automatically collected, creating a self-improving loop without additional human labeling effort. This presents a compelling value proposition for technical decision-makers: the ability to deploy smaller, more affordable models that perform comparably to their premium counterparts on domain-specific tasks, without the traditionally required extensive labeling costs. This approach particularly benefits organizations with rich troves of unstructured data and domain-specific requirements but limited resources for manual labeling – precisely the position in which many enterprises find themselves.
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