As you deploy generative AI applications to diverse user groups, you might face a significant challenge that impacts user safety and application reliability: verifying each AI response is appropriate, accurate, and safe for the specific user receiving it. Content...
This post is a collaboration between AWS and Pipecat.
Deploying intelligent voice agents that maintain natural, human-like conversations requires streaming to users where they are, across web, mobile, and phone channels, even under heavy traffic and unreliable network conditions. Even...
Video content is now everywhere, from security surveillance and media production to social platforms and enterprise communications. However, extracting meaningful insights from large volumes of video remains a major challenge. Organizations need solutions that can understand not only what...
In December 2025, we announced the availability of Reinforcement fine-tuning (RFT) on Amazon Bedrock starting with support for Nova models. This was followed by extended support for Open weight models such as OpenAI GPT OSS 20B and Qwen 3...
Deploying large language models (LLMs) for inference requires reliable GPU capacity, especially during critical evaluation periods, limited-duration production testing, or burst workloads. Capacity constraints can delay deployments and impact application performance. Customers can use Amazon SageMaker AI training plans...
Businesses across industries face a common challenge: how to efficiently extract valuable information from vast amounts of unstructured data. Traditional approaches often involve resource-intensive processes and inflexible models. This post introduces a game-changing solution: Claude Tool use in Amazon...
