The TAO of data: How Databricks is optimizing  AI LLM fine-tuning without data labels

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More AI models perform only as well as the data used to train or fine-tune them. Labeled data has been a foundational element of machine learning (ML) and generative AI for much of their history. Labeled data is information tagged to help AI models understand context during training. As enterprises race to implement AI applications, the hidden bottleneck often isn’t technology – it’s the months-long process of collecting, curating and labeling domain-specific data. This “data labeling tax” has forced technical leaders to choose between delaying deployment or accepting suboptimal performance from generic models. Databricks is taking direct aim at that challenge. This week, the company released research on a new approach called Test-time Adaptive Optimization (TAO). The basic idea behind the approach is to enable enterprise-grade large language model (LLM) tuning ... Read full article.