Скачать с ютуб Efficiently Scaling and Deploying LLMs // Hanlin Tang // LLM's in Production Conference в хорошем качестве

Efficiently Scaling and Deploying LLMs // Hanlin Tang // LLM's in Production Conference

// Abstract Hanlin discusses the evolution of Large Language Models and the importance of efficient scaling and deployment. He emphasizes the benefits of a decentralized approach of many small specialized models over one giant AGI model controlled by a few companies. Hanlin explains the advantages of companies training their own custom models, such as data privacy concerns, and provides insights into when it is appropriate to build your own models and the available tooling for training and deployment. // Bio Hanlin is the CTO & Co-founder of MosaicML, an ML infrastructure startup that enables enterprises to easily train large-scale AI models in their secure environments. Hanlin was previously the Director of the Intel AI Lab, responsible for the research and deployment of deep learning models. He joined Intel from its acquisition of Nervana Systems. Hanlin has a Ph.D. from Harvard University and has published in leading journals and conferences such as NeurIPS, ICLR, ICML, Neuron, and PNAS.