Google is investing in infrastructure and an AI-ready workforce in Oklahoma.
Google is investing an additional $9 billion in Oklahoma within the next two years in cloud and AI infrastructure. This investment supports the development of a new data…
PwC and AWS Build Responsible AI with Automated Reasoning on Amazon Bedrock
This is a guest post co-written with Scott Likens, Ambuj Gupta, Adam Hood, Chantal Hudson, Priyanka Mukhopadhyay, Deniz Konak Ozturk, and Kevin Paul from PwC Organizations are deploying generative AI solutions while balancing accuracy, security, and compliance. In this globally competitive environment, scale matters less, speed matters more, and innovation matters most of all, according …
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How Amazon scaled Rufus by building multi-node inference using AWS Trainium chips and vLLM
At Amazon, our team builds Rufus, a generative AI-powered shopping assistant that serves millions of customers at immense scale. However, deploying Rufus at scale introduces significant challenges that must be carefully navigated. Rufus is powered by a custom-built large language model (LLM). As the model’s complexity increased, we prioritized developing scalable multi-node inference capabilities that …
Build an intelligent financial analysis agent with LangGraph and Strands Agents
Agentic AI is revolutionizing the financial services industry through its ability to make autonomous decisions and adapt in real time, moving well beyond traditional automation. Imagine an AI assistant that can analyze quarterly earnings reports, compare them against industry expectations, and generate insights about future performance. This seemingly straightforward task involves multiple complex steps: document …
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Amazon Bedrock AgentCore Memory: Building context-aware agents
AI assistants that forget what you told them 5 minutes ago aren’t very helpful. While large language models (LLMs) excel at generating human-like responses, they are fundamentally stateless—they don’t retain information between interactions. This forces developers to build custom memory systems to track conversation history, remember user preferences, and maintain context across sessions, often solving …
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Build a conversational natural language interface for Amazon Athena queries using Amazon Nova
Data analysis often presents significant challenges for business users who aren’t proficient in SQL. Traditional methods require technical expertise to query databases, leading to delayed insights and dependence on data teams. Many organizations struggle with making their data accessible to business users while maintaining the analytical capabilities of Amazon Athena. Modern AI agents are transforming …
Train and deploy AI models at trillion-parameter scale with Amazon SageMaker HyperPod support for P6e-GB200 UltraServers
Imagine harnessing the power of 72 cutting-edge NVIDIA Blackwell GPUs in a single system for the next wave of AI innovation, unlocking 360 petaflops of dense 8-bit floating point (FP8) compute and 1.4 exaflops of sparse 4-bit floating point (FP4) compute. Today, that’s exactly what Amazon SageMaker HyperPod delivers with the launch of support for …
How Indegene’s AI-powered social intelligence for life sciences turns social media conversations into insights
This post is co-written with Rudra Kannemadugu and Shravan K S from Indegene Limited. In today’s digital-first world, healthcare conversations are increasingly happening online. Yet the life sciences industry has struggled to keep pace with this shift, facing challenges in effectively analyzing and deriving insights from complex medical discussions on a scale. This post will …
Unlocking enhanced legal document review with Lexbe and Amazon Bedrock
This post is co-authored with Karsten Weber and Rosary Wang from Lexbe. Legal professionals are frequently tasked with sifting through vast volumes of documents to identify critical evidence for litigation. This process can be time-consuming, prone to human error, and expensive—especially when tight deadlines loom. Lexbe, a leader in legal document review software, confronted these …
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Automate AIOps with SageMaker Unified Studio Projects, Part 2: Technical implementation
In Part 1 of our series, we established the architectural foundation for an enterprise artificial intelligence and machine learning (AI/ML) configuration with Amazon SageMaker Unified Studio projects. We explored the multi-account structure, project organization, multi-tenancy approaches, and repository strategies needed to create a governed AI development environment. In this post, we focus on implementing this …
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