AWS for Industries

Category: Uncategorized

Scaling ML in production: how BBVA accelerated delivery with MLOps

Scaling ML in production: how BBVA accelerated delivery with MLOps

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.

Accelerate RISC-V Software Development Before Silicon: Virtual Prototyping with MachineWare’s SIM-V on AWS

Software engineering teams building for RISC-V architectures often wait months for hardware prototypes before they can start development and evaluations. This post shows how MachineWare’s SIM-V — an ultra-fast RISC-V Virtual Platform — runs on Amazon Web Services, Inc. (AWS), so software teams can develop, debug, and validate RISC-V software long before silicon is available. […]

Deploy diagnostic-quality imaging globally with MedDream and AWS HealthImaging

Deploy diagnostic-quality imaging globally with MedDream and AWS HealthImaging

Learn how by combining MedDream’s FDA-cleared diagnostic viewer with AWS HealthImaging and automated AWS Cloud Development Kit (AWS CDK) deployment, you can deploy a complete, production-ready medical imaging solution in under an hour—achieving sub-second loading times and reducing storage costs by up to 40% compared to on-premises infrastructure.

Coins in Motion: Building agentic blockchain payments for in-vehicle experiences

Coins in Motion: Building agentic blockchain payments for in-vehicle experiences

Agentic blockchain-based payments are poised to transform in-vehicle driving experiences. As vehicles become increasingly connected and autonomous, they are evolving from passive transportation tools into active economic agents capable of conducting their own financial transactions [see HBR Article, 2021]. Imagine your car automatically paying for highway tolls, electric charging sessions, parking fees, or even purchasing […]

Surgical Intelligence Engine with AWS and NVIDIA — Edge-to-Cloud Technology for Med Techs

Edge-to-Cloud Architecture for Real-Time Surgical Intelligence with AWS and NVIDIA

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.

Reimagining B-Pillar DFMEA: Why Ontology-Grounded AI Is the Future of Automotive Engineering

Reimagining B-Pillar DFMEA: Why Ontology-Grounded AI Is the Future of Automotive Engineering

This two-part series explores how ontology-grounded agentic AI transforms Design Failure Mode and Effects Analysis (DFMEA) for safety-critical automotive components — from the strategic imperative driving adoption to the architectural patterns and implementation details. In this post, we focus on: How AI can help with DFMEA, how engineering ontologies enable AI to reason for failure mechanisms rather than pattern-match, and what engineering leaders should prioritize today.

Flexible Telecom AI Workload Deployment Across AWS Hybrid Cloud

Flexible Telecom AI Workload Deployment Across AWS Hybrid Cloud

This blog introduces a structured placement approach for AI workloads across AWS hybrid infrastructure. By evaluating each AI lifecycle phase against four dimensions (data sovereignty, latency, data gravity, and operational readiness), architects can determine the optimal deployment tier among AWS Regions, AWS Local Zones, AWS Outposts and AWS AI Factories.

Building a HIPAA-ready generative AI architecture for healthcare on AWS

Building a HIPAA-ready generative AI architecture for healthcare on AWS

In this post, we describe a comprehensive, HIPAA-ready generative AI architecture for healthcare on Amazon Web Services (AWS) using a defense-in-depth approach. By layering compliance controls at multiple distinct levels, this architecture creates a system where no single point of failure compromises patient data protection, and each component that touches ePHI is independently auditable.

Build an AI-powered 5G Signaling Trace Analyzer Using Amazon Bedrock

Build an AI-powered 5G Signaling Trace Analyzer Using Amazon Bedrock

Telecom engineers routinely analyze decoded signaling traces to troubleshoot network issues, validate procedure execution, and accelerate root cause analysis. In 5G networks, even a single registration attempt can span multiple layers such as NAS and NGAP. The registration attempt includes security context establishment and carries identifiers such as Subscription Concealed Identifier (SUCI), Globally Unique Temporary […]