AWS (Page 2)

In: Artificial Intelligence

Fine-tuning of large language models (LLMs) has emerged as a crucial technique for organizations seeking to adapt powerful foundation models (FMs) to their specific needs. Rather than training models from scratch—a process that can cost millions of dollars and require extensive computational resources—companies can customize existing models with domain-specific dataContinue Reading

In: Artificial Intelligence

In 2024, the Ministry of Economy, Trade and Industry (METI) launched the Generative AI Accelerator Challenge (GENIAC)—a Japanese national program to boost generative AI by providing companies with funding, mentorship, and massive compute resources for foundation model (FM) development. AWS was selected as the cloud provider for GENIAC’s second cycleContinue Reading

In: Artificial Intelligence

This post was written with Zach Heath of Kyruus Health. When health plan members need care, they shouldn’t need a dictionary. Yet millions face this exact challenge—describing symptoms in everyday language while healthcare references clinical terminology and complex specialty classifications. This disconnect forces members to become amateur medical translators, attemptingContinue Reading

In: Artificial Intelligence

This post is co-written with Andrew Liu, Chelsea Isaac, Zoey Zhang, and Charlie Huang from NVIDIA. DGX Cloud on Amazon Web Services (AWS) represents a significant leap forward in democratizing access to high-performance AI infrastructure. By combining NVIDIA GPU expertise with AWS scalable cloud services, organizations can accelerate their time-to-train,Continue Reading

In: Artificial Intelligence

When we launched the AWS Generative AI Innovation Center in 2023, we had one clear goal: help customers turn AI potential into real business value. We’ve already guided thousands of customers across industries from financial services to healthcare—including Formula 1, FOX, GovTech Singapore, Itaú Unibanco, Nasdaq, NFL, RyanAir, and S&PContinue Reading

In: Artificial Intelligence

Data is your generative AI differentiator, and successful generative AI implementation depends on a robust data strategy incorporating a comprehensive data governance approach. Traditional data architectures often struggle to meet the unique demands of generative such as applications. An effective generative AI data strategy requires several key components like seamlessContinue Reading

In: Artificial Intelligence

Imagine a system that can explore multiple approaches to complex problems, drawing on its understanding of vast amounts of data, from scientific datasets to source code to business documents, and reasoning through the possibilities in real time. This lightning-fast reasoning isn’t waiting on the horizon. It’s happening today in ourContinue Reading

In: Artificial Intelligence

Generative AI continues to reshape how businesses approach innovation and problem-solving. Customers are moving from experimentation to scaling generative AI use cases across their organizations, with more businesses fully integrating these technologies into their core processes. This evolution spans across lines of business (LOBs), teams, and software as a serviceContinue Reading