Mark Roy

Join this session to learn how Northwestern Mutual transformed their application development support system using Amazon Bedrock multi-agent collaboration with better planning and communication among agents.

With a robust understanding of natural language and the ability to be further customized to domain-specific tasks, foundation models (FMs) are incredibly powerful.

Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, share, and manage features for machine learning (ML) models. Features are used repeatedly by multiple teams, and feature quality is critical to ensure a highly accurate model.

Modern machine-learning workflows leverage AWS services such as Amazon Transcribe and Amazon Comprehend to extract, validate, mutate, and enrich your data. Some drive transactional systems that use ML to generate metadata, others derive insights by visualizing customer-interaction sentiment.

Amazon SageMaker helps provide the best model performance for less cost. In this session, we walk through a TCO analysis of Amazon SageMaker, exploring its three modules—build, train, and deploy.

Amazon SageMaker helps provide the best model performance for less cost. In this session, we walk through a TCO analysis of Amazon SageMaker, exploring its three modules—build, train, and deploy.