Accelerating genomics variant interpretation with AWS HealthOmics and Amazon Bedrock AgentCore

Favorite Genomic research stands at a transformative crossroads where the exponential growth of sequencing data demands equally sophisticated analytical capabilities. According to the 1000 Genomes Project, a typical human genome differs from the reference at 4.1–5.0 million sites, with most variants being SNPs and short indels. These variants, when aggregated

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Shared by AWS Machine Learning November 20, 2025

MSD explores applying generative Al to improve the deviation management process using AWS services

Favorite This post is co-written with Hossein Salami and Jwalant Vyas from MSD.  In the biopharmaceutical industry, deviations in the manufacturing process are rigorously addressed. Each deviation is thoroughly documented, and its various aspects and potential impacts are closely examined to help ensure drug product quality, patient safety, and compliance.

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Shared by AWS Machine Learning November 20, 2025

Bringing tic-tac-toe to life with AWS AI services

Favorite Large language models (LLMs) now support a wide range of use cases, from content summarization to the ability to reason about complex tasks. One exciting new topic is taking generative AI to the physical world by applying it to robotics and physical hardware. Inspired by this, we developed a

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Shared by AWS Machine Learning November 19, 2025

Using Spectrum fine-tuning to improve FM training efficiency on Amazon SageMaker AI

Favorite Optimizing generative AI applications relies on tailoring foundation models (FMs) using techniques such as prompt engineering, RAG, continued pre-training, and fine-tuning. Efficient fine-tuning is achieved by strategically managing hardware, training time, data volume, and model quality to reduce resource demands and maximize value. Spectrum is a new approach designed

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Shared by AWS Machine Learning November 19, 2025

Announcing the AWS Well-Architected Responsible AI Lens 

Favorite As AI applications grow more complex, many builders struggle to appropriately and responsibly balance AI benefits and risks. Few resources exist that help non-experts articulate and resolve the key design decisions they must make. However, it doesn’t have to be this way. Today, we’re announcing the AWS Well-Architected Responsible

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Shared by AWS Machine Learning November 19, 2025

HyperPod enhances ML infrastructure with security and storage

Favorite Amazon SageMaker HyperPod is a purpose-built infrastructure for optimizing foundation model training and inference at scale. SageMaker HyperPod removes the undifferentiated heavy lifting involved in building and optimizing machine learning (ML) infrastructure for training foundation models (FMs). As AI moves towards deployment adopting to a multitude of domains and

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Shared by AWS Machine Learning November 18, 2025