AWS for Industries
Author: Brian Loyal
Brian Loyal is a Senior AI/ML Solutions Architect in the Global Healthcare and Life Sciences team at Amazon Web Services. He has more than 16 years experience in biotechnology and machine learning and is passionate about helping customers solve genomic and proteomic challenges. In his spare time, he enjoys cooking and eating with his friends and family.
Accelerating Life Sciences Innovation with Agentic AI on AWS
In the rapidly evolving field of life sciences, organizations are increasingly turning to agentic AI to streamline complex workflows, enhance collaboration, and accelerate research outcomes. With recent advancements in foundation models, enterprise-grade infrastructure, and developer tools, building intelligent agents is now more accessible, scalable, and impactful than ever. With a track record of helping the […]
Choice: Keeping pace with emerging models for generative AI in Life Sciences
Embracing generative AI is imperative for life sciences organizations to stay competitive. However, the rapid evolution of models and data strategies has been overwhelming. While this diverse array of choices enhances business outcomes, life sciences leaders face the daunting task of strategizing for a future amid new AI breakthroughs emerging every week. At the recent […]
Find the Next Blockbuster with NVIDIA BioNeMo Framework on Amazon SageMaker
The NVIDIA BioNeMo framework accelerates drug discovery by giving researchers access to powerful foundation models (FMs) for biology. By integrating BioNeMo with Amazon SageMaker, researchers can develop cutting-edge AI solutions with the scalability, data security, and operational excellence they expect from AWS. Introduction Drug R&D is a long, costly, and risky process. On average, it […]
Predicting protein structures at scale using AWS Batch
Proteins are large biomolecules that play an important role in the body. Knowing the physical structure of proteins is key to understanding their function. However, it can be difficult and expensive to determine the structure of many proteins experimentally. One alternative is to predict these structures using machine learning algorithms. Several high-profile research teams have […]