GenAI News

This is Part II of a two-part series from the AWS Generative AI Innovation Center. If you missed Part I, refer to Operationalizing Agentic...

We thank Greg Pereira and Robert Shaw from the llm-d team for their support in bringing llm-d to AWS. In the agentic and reasoning era,...

Building and managing machine learning (ML) features at scale is one of the most critical and complex challenges in modern data science workflows. Organizations...

This post is cowritten with Ilija Subanovic and Michael Rice from Workhuman. Workhuman’s customer service and analytics team were drowning in one-time reporting requests from...

EAGLE is the state-of-the-art method for speculative decoding in large language model (LLM) inference, but its autoregressive drafting creates a hidden bottleneck: the more...

Deploying AI agents safely in regulated industries is challenging. Without proper boundaries, agents that access sensitive data or execute transactions can pose significant security...

As organizations scale their generative AI workloads on Amazon Bedrock, operational visibility into inference performance and resource consumption becomes critical. Teams running latency-sensitive applications...

This post is a collaboration between AWS, NVIDIA and Heidi.  Automatic speech recognition (ASR), often called speech-to-text (STT) is becoming increasingly critical across industries like...

This post shows you how to build a scalable multimodal video search system that enables natural language search across large video datasets using Amazon...

Agentic AI isn’t a feature you turn on. It’s a shift in how work is defined, who does it, and how decisions get made. Most...