AWS and NVIDIA deepen strategic collaboration to accelerate AI from pilot to production
AI is moving fast, and for most of our customers, the real […]
Read moreCategory: Machine learning
Agentic AI in the Enterprise Part 2: Guidance by Persona
This is Part II of a two-part series from the AWS Generative […]
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Introducing Disaggregated Inference on AWS powered by llm-d
We thank Greg Pereira and Robert Shaw from the llm-d team for […]
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Build an offline feature store using Amazon SageMaker Unified Studio and SageMaker Catalog
Building and managing machine learning (ML) features at scale is one of […]
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How Workhuman built multi-tenant self-service reporting using Amazon Quick Sight embedded dashboards
This post is cowritten with Ilija Subanovic and Michael Rice from Workhuman. […]
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P-EAGLE: Faster LLM inference with Parallel Speculative Decoding in vLLM
EAGLE is the state-of-the-art method for speculative decoding in large language model […]
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Secure AI agents with Policy in Amazon Bedrock AgentCore
Deploying AI agents safely in regulated industries is challenging. Without proper boundaries, […]
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Improve operational visibility for inference workloads on Amazon Bedrock with new CloudWatch metrics for TTFT and Estimated Quota Consumption
As organizations scale their generative AI workloads on Amazon Bedrock, operational visibility […]
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Fine-tuning NVIDIA Nemotron Speech ASR on Amazon EC2 for domain adaptation
This post is a collaboration between AWS, NVIDIA and Heidi. Automatic speech […]
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Multimodal embeddings at scale: AI data lake for media and entertainment workloads
This post shows you how to build a scalable multimodal video search […]
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