Abstract: Large-scale deep models drive the most innovative and exciting AI technologies like Alexa, Dalle, and Github's Copilot. Rajiv shares how large-scale models are reshaping what it means to do data science with an enterprise. This talk will give you the understanding and tools to take advantage of these new machine learning techniques. Large-scale models allow us to move beyond discriminative models and incorporate generative models. Models are now capable of solving multiple tasks simultaneously. However, these powerful models carry risks associated with bias and unethical use. All of these are new challenges for most classically trained data scientists. After going through the origins of large-scale models and transformers, Rajiv will share examples of how enterprises are unlocking value in new use cases, such as auto-completion, semantic search, and document AI. The talk will also incorporate a notebook, code snippets, and paper references so anyone can get started.
Bio: Rajiv Shah is a leading expert on practical AI. At Hugging Face, his primary focus is on enabling enterprises to succeed with AI. He previously led data science enablement efforts across hundreds of data scientists at DataRobot and has been part of data science teams at Snorkel AI, Caterpillar, and State Farm. He is a widely recognized speaker on AI, has received many patents, and published research papers in several domains, including sports analytics, deep learning, and interpretability. He received a Ph.D. and a J.D. from the University of Illinois at Urbana Champaign.
Machine Learning Engineer | Hugging Face