Favorite 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
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Shared by AWS Machine Learning August 14, 2025
Favorite 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
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Shared by AWS Machine Learning August 14, 2025
Favorite 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
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Shared by AWS Machine Learning August 14, 2025
Favorite Organizations are increasingly excited about the potential of AI agents, but many find themselves stuck in what we call “proof of concept purgatory”—where promising agent prototypes struggle to make the leap to production deployment. In our conversations with customers, we’ve heard consistent challenges that block the path from experimentation
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Shared by AWS Machine Learning August 14, 2025
Favorite 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… View Original Source (blog.google/technology/ai/) Here.
Favorite Amazon SageMaker Unified Studio represents the evolution towards unifying the entire data, analytics, and artificial intelligence and machine learning (AI/ML) lifecycle within a single, governed environment. As organizations adopt SageMaker Unified Studio to unify their data, analytics, and AI workflows, they encounter new challenges around scaling, automation, isolation, multi-tenancy,
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Shared by AWS Machine Learning August 13, 2025
Favorite 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
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Shared by AWS Machine Learning August 13, 2025
Favorite 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
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Shared by AWS Machine Learning August 13, 2025
Favorite 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
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Shared by AWS Machine Learning August 13, 2025
Favorite 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
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Shared by AWS Machine Learning August 13, 2025
Shared by AWS Machine Learning August 14, 2025
