Agenda

Today, large language or Foundation Models (FMs) represent one of the most powerful new ways to build AI models; however, they still struggle to achieve production-level accuracy out of the box on complex, high-value, and/or dynamic use cases, often “hallucinating” facts, propagating data biases, and misclassifying domain-specific edge cases.  This “last mile” problem is always the hardest part of shipping real AI applications, especially in the enterprise- and while FMs provide powerful foundations, they do not “build the house”.

In this talk, I’ll provide an overview of how this last mile adaptation is increasingly all about the data (not eg. the model architecture, hyperparameters, or algorithms), and give an overview of modern data-centricAI development approaches to solve this and preview new state of the art techniques and tools for handling all stages of data-centric development for foundation models, from pre-training to instruction-tuning and alignment, to task-specific fine tuning and distillation.