AI

How Deloitte Italy built a digital payments fraud detection solution using quantum machine learning and Amazon Braket

As digital commerce expands, fraud detection has become critical in protecting businesses and consumers engaging in online transactions. Implementing machine learning (ML) algorithms enables real-time analysis of high-volume transactional data to rapidly identify fraudulent activity. This advanced capability helps mitigate financial risks and safeguard customer privacy within expanding digital markets. Deloitte is a strategic global […]

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Amazon SageMaker unveils the Cohere Command R fine-tuning model

AWS announced the availability of the Cohere Command R fine-tuning model on Amazon SageMaker. This latest addition to the SageMaker suite of machine learning (ML) capabilities empowers enterprises to harness the power of large language models (LLMs) and unlock their full potential for a wide range of applications. Cohere Command R is a scalable, frontier

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Derive meaningful and actionable operational insights from AWS Using Amazon Q Business

As a customer, you rely on Amazon Web Services (AWS) expertise to be available and understand your specific environment and operations. Today, you might implement manual processes to summarize lessons learned, obtain recommendations, or expedite the resolution of an incident. This can be time consuming, inconsistent, and not readily accessible. This post shows how to

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AI method radically speeds predictions of materials’ thermal properties

It is estimated that about 70 percent of the energy generated worldwide ends up as waste heat. If scientists could better predict how heat moves through semiconductors and insulators, they could design more efficient power generation systems. However, the thermal properties of materials can be exceedingly difficult to model. The trouble comes from phonons, which

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Accelerate your generative AI distributed training workloads with the NVIDIA NeMo Framework on Amazon EKS

In today’s rapidly evolving landscape of artificial intelligence (AI), training large language models (LLMs) poses significant challenges. These models often require enormous computational resources and sophisticated infrastructure to handle the vast amounts of data and complex algorithms involved. Without a structured framework, the process can become prohibitively time-consuming, costly, and complex. Enterprises struggle with managing

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Governing the ML lifecycle at scale, Part 2: Multi-account foundations

Your multi-account strategy is the core of your foundational environment on AWS. Design decisions around your multi-account environment are critical for operating securely at scale. Grouping your workloads strategically into multiple AWS accounts enables you to apply different controls across workloads, track cost and usage, reduce the impact of account limits, and mitigate the complexity

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