Building and managing machine learning (ML) features at scale is one of the most critical and complex challenges in modern data science workflows. Organizations often struggle with fragmented feature pipelines, …
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 tokens that you speculate, the more …
P-EAGLE: Faster LLM inference with Parallel Speculative Decoding in vLLM Read More »
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 must understand how quickly models begin …
Deploying AI agents safely in regulated industries is challenging. Without proper boundaries, agents that access sensitive data or execute transactions can pose significant security risks. Unlike traditional software, an AI …
Secure AI agents with Policy in Amazon Bedrock AgentCore Read More »
This post shows you how to build a scalable multimodal video search system that enables natural language search across large video datasets using Amazon Nova models and Amazon OpenSearch Service. …
Multimodal embeddings at scale: AI data lake for media and entertainment workloads Read More »
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 healthcare, customer service, and media …
Fine-tuning NVIDIA Nemotron Speech ASR on Amazon EC2 for domain adaptation Read More »
A new Google AI initiative aims to improve heart health outcomes for people living in remote Australian communities.
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 enterprises learn this the hard …
Operationalizing Agentic AI Part 1: A Stakeholder’s Guide Read More »
This post is cowritten by David Stewart and Matthew Persons from Oumi. Fine-tuning open source large language models (LLMs) often stalls between experimentation and production. Training configurations, artifact management, and …
Accelerate custom LLM deployment: Fine-tune with Oumi and deploy to Amazon Bedrock Read More »
Today we announced new beta features for Gemini in Sheets to help you create, organize and edit entire sheets, from basic tasks to complex data analysis — just describe …
