In this comprehensive video, AWS machine learning specialist Emily Webber introduces the process of pretraining foundation models on AWS. She explains when and why to create a new foundation model, comparing it to fine-tuning existing models. Webber discusses the data requirements, compute resources, and business justifications needed for pretraining projects. She then delves into distributed training techniques on Amazon SageMaker, including data parallelism and model parallelism. The video concludes with a detailed walkthrough of pretraining a 30 billion parameter GPT-2 model using SageMaker's distributed training capabilities. Viewers can access accompanying notebook resources to follow along with the demonstration.