Generative AI foundation model training on Amazon SageMaker

To stay competitive, businesses across industries use foundation models (FMs) to transform their applications. Although FMs offer impressive out-of-the-box capabilities, achieving a true competitive edge often requires deep model customization through pre-training or fine-tuning. However, these approaches demand advanced AI expertise, high performance compute, fast storage access and can be prohibitively expensive for many organizations.

In this post, we explore how organizations can address these challenges and cost-effectively customize and adapt FMs using AWS managed services such as Amazon SageMaker training jobs and Amazon SageMaker HyperPod. We discuss how these powerful tools enable organizations to optimize compute resources and reduce the complexity of model training and fine-tuning. We explore how you can make an informed decision about which Amazon SageMaker service is most applicable to your business needs and requirements.

Business challenge

Businesses today face numerous challenges in effectively implementing and managing machine learning (ML) initiatives. These challenges include scaling ...