The journey from taking a foundation model (FM) from experimentation to production is filled with choices, decisions, and pitfalls that can increase undifferentiated heavylifting and delay time-to-market. In this session, learn how purpose built capabilities in Amazon SageMaker can help ML practitioners pre-train, evaluate, and fine-tune FMs with advanced techniques, and deploy FMs with fine-grain controls for generative AI use cases that have stringent requirements on accuracy, latency, and cost. Join us to learn how to simplify the generative AI journey, follow best practices, save time and cost, and shorten time-to-market.
Ankur Mehrotra
Ankur is a GM at AWS Machine Learning and leads foundational SageMaker services such as SageMaker Studio, Notebooks, Training, Inference, Feature Store, MLOps, etc. Before SageMaker, he led AI services for personalization, forecasting, healthcare & life sciences, edge AI devices and SDKs, as well as thought leadership programs such as AWS DeepRacer. Ankur has worked at Amazon for over 15 years. Before joining AWS, he spent several years in Amazon’s Consumer organization, where he led the development of automated marketing/advertising systems, as well as automated pricing systems.