Faster answers, better decisions: ML-powered document analysis
Scalable, governed LLMs with Domino and AWS
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In regulated industries like life sciences, accelerating research while ensuring compliance is crucial. With LLMs and Retrieval Augmented Generation (RAG), researchers can ask real-time questions about clinical trial documents, extracting insights that drive faster, data-backed decisions.
Join Domino as we demonstrate how to use LLMs and RAG for clinical trial summarization – unifying data, compute, and AI tool management to optimize performance, cut costs, and accelerate AI from experimentation to production.
In this webinar, we’ll show how to use Domino to:
- Deploy LLMs to Amazon SageMaker for scalable, governed inference.
- Use AWS Trainium and Inferentia for accelerated training and inference.
- Access large language models via Amazon Bedrock, backed by Domino’s governance capabilities.
- Centralize model development and monitoring through Domino’s unified platform.
Featured Speakers
Josh Mineroff
Director of Solution Architecture, Technology Partners
Josh Mineroff works with Domino's technology partners such as AWS to bring cutting-edge AI solutions from the data and analytics ecosystem to leading data science teams. Prior to joining Domino in 2019, he founded Meet n’ Treat — a dog socialization platform. During his Ph.D. in applied optimization, he published papers on heart valve design and uncertainty quantification generating over 350 cumulative citations.
James Yi
Senior AI/ML Partner Solutions Architect
James Yi is a Senior AI/ML Partner Solutions Architect in the Technology Partners COE Tech team at Amazon Web Services. He is passionate about working with enterprise customers and partners to design, deploy, and scale AI/ML applications to derive business value. Outside of work, he enjoys playing soccer, traveling, and spending time with his family.