Category: Amazon SageMaker

This post demonstrates on Massive Multitask Language Understanding (MMLU) [Hendrycks et al., 2021], an open-source multiple-choice benchmark accessible via Hugging Face Hub, selecting four business-relevant subjects.

In this post, we show how you can use AWS HealthLake to meet CMS-0057-F requirements while building a FHIR data solution that accelerates your broader digital transformation.

In this post, we build on that foundation by constructing specialized AI agents for each data modality along with a supervisor agent that orchestrates cross-modal analysis using Amazon Bedrock AgentCore and Strands Agents SDK. We also train predictive AI models with Amazon SageMaker AI to predict patient outcomes from multimodal features. To further explore the implementation details and get hands-on experience, refer to the accompanying code repository.

In this two-part blog series, we show how you can build agents that interact with multimodal HCLS data, making it easier for end users to query, explore, and ask questions of the data. We build on previous guidance for multimodal data analysis, which demonstrates how to store, query, and analyze clinical, genomic, and medical imaging data using purpose-built AWS services.

This post describes how BBVA used pilots to identify reusable ML patterns, standardize operational workflows, and design extensible MLOps templates that accelerate ML delivery while maintaining governance and flexibility across teams and business domains.

Learn how BBVA leverages AWS technologies that provide the technical foundation while supporting the bank’s high standards for governance, risk management and auditability.

Every major industrial facility relies on thousands of highly complex technical drawings called Piping and Instrumentation Diagrams (P&IDs) that serve as the DNA of industrial operations. These diagrams show how equipment connects, materials flow, and safety systems protect workers and assets. These diagrams are complex. For example, where 511 industrial P&ID documents may mean 1,397,710 […]

Learn how to architect an end-to-end pipeline that processes surgical video at the edge for de-identification, instrument detection, and surgical phase recognition—while using the cloud for model training and fleet management.

Learn how at the 2026 AWS Life Sciences Symposium, we brought together some of the most innovative companies in MedTech to share what it takes to build in this environment, and what becomes possible when you have the right data foundation and AI backbone.

In this post, we show how BridgeWise was able to overcome these challenges when developing their wealth AI platform with responsible AI in mind. BridgeWise is a global leader in AI for wealth.