AI

Accelerating LLM fine-tuning with unstructured data using SageMaker Unified Studio and S3

Last year, AWS announced an integration between Amazon SageMaker Unified Studio and Amazon S3 general purpose buckets. This integration makes it straightforward for teams to use unstructured data stored in Amazon Simple Storage Service (Amazon S3) for machine learning (ML) and data analytics use cases. In this post, we show how to integrate S3 general […]

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Introducing Amazon Polly Bidirectional Streaming: Real-time speech synthesis for conversational AI

Building natural conversational experiences requires speech synthesis that keeps pace with real-time interactions. Today, we’re excited to announce the new Bidirectional Streaming API for Amazon Polly, enabling streamlined real-time text-to-speech (TTS) synthesis where you can start sending text and receiving audio simultaneously. This new API is built for conversational AI applications that generate text or

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Augmenting citizen science with computer vision for fish monitoring

Each spring, river herring populations migrate from Massachusetts coastal waters to begin their annual journey up rivers and streams to freshwater spawning habitat. River herring have faced severe population declines over the past several decades, and their migration is extensively monitored across the region, primarily through traditional visual counting and volunteer-based programs.  Monitoring fish movement and

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Unlocking video insights at scale with Amazon Bedrock multimodal models

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 appears in a video, but also the context, narrative, and underlying meaning of the content. In

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Deploy voice agents with Pipecat and Amazon Bedrock AgentCore Runtime – Part 1

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 small delays can break the conversational flow, causing users to perceive the agent as unresponsive

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