AWS Machine Learning Blog

Category: Amazon Bedrock

Accelerate video Q&A workflows using Amazon Bedrock Knowledge Bases, Amazon Transcribe, and thoughtful UX design

The solution presented in this post demonstrates a powerful pattern for accelerating video and audio review workflows while maintaining human oversight. By combining the power of AI models in Amazon Bedrock with human expertise, you can create tools that not only boost productivity but also maintain the critical element of human judgment in important decision-making processes.

Harnessing Amazon Bedrock generative AI for resilient supply chain

By leveraging the generative AI capabilities and tooling of Amazon Bedrock, you can create an intelligent nerve center that connects diverse data sources, converts data into actionable insights, and creates a comprehensive plan to mitigate supply chain risks. This post walks through how Amazon Bedrock Flows connects your business systems, monitors medical device shortages, and provides mitigation strategies based on knowledge from Amazon Bedrock Knowledge Bases or data stored in Amazon S3 directly. You’ll learn how to create a system that stays ahead of supply chain risks.

How Travelers Insurance classified emails with Amazon Bedrock and prompt engineering

In this post, we discuss how FMs can reliably automate the classification of insurance service emails through prompt engineering. When formulating the problem as a classification task, an FM can perform well enough for production environments, while maintaining extensibility into other tasks and getting up and running quickly. All experiments were conducted using Anthropic’s Claude models on Amazon Bedrock.

DeepSeek-R1 model now available in Amazon Bedrock Marketplace and Amazon SageMaker JumpStart

DeepSeek-R1 is an advanced large language model that combines reinforcement learning, chain-of-thought reasoning, and a Mixture of Experts architecture to deliver efficient, interpretable responses while maintaining safety through Amazon Bedrock Guardrails integration.

Streamline grant proposal reviews using Amazon Bedrock

The AWS Social Responsibility & Impact (SRI) team recognized an opportunity to augment this function using generative AI. The team developed an innovative solution to streamline grant proposal review and evaluation by using the natural language processing (NLP) capabilities of Amazon Bedrock. In this post, we explore the technical implementation details and key learnings from the team’s Amazon Bedrock powered grant proposal review solution, providing a blueprint for organizations seeking to optimize their grants management processes.

Aetion Services

How Aetion is using generative AI and Amazon Bedrock to unlock hidden insights about patient populations

In this post, we review how Aetion’s Smart Subgroups Interpreter enables users to interact with Smart Subgroups using natural language queries. Powered by Amazon Bedrock and Anthropic’s Claude 3 large language models (LLMs), the interpreter responds to user questions expressed in conversational language about patient subgroups and provides insights to generate further hypotheses and evidence.

Deploy DeepSeek-R1 distilled Llama models with Amazon Bedrock Custom Model Import

In this post, we demonstrate how to deploy distilled versions of DeepSeek-R1 models using Amazon Bedrock Custom Model Import. We focus on importing the variants currently supported DeepSeek-R1-Distill-Llama-8B and DeepSeek-R1-Distill-Llama-70B, which offer an optimal balance between performance and resource efficiency.

Generative AI operating models in enterprise organizations with Amazon Bedrock

As generative AI adoption grows, organizations should establish a generative AI operating model. An operating model defines the organizational design, core processes, technologies, roles and responsibilities, governance structures, and financial models that drive a business’s operations. In this post, we evaluate different generative AI operating model architectures that could be adopted.

Optimizing AI responsiveness: A practical guide to Amazon Bedrock latency-optimized inference

In this post, we explore how Amazon Bedrock latency-optimized inference can help address the challenges of maintaining responsiveness in LLM applications. We’ll dive deep into strategies for optimizing application performance and improving user experience. Whether you’re building a new AI application or optimizing an existing one, you’ll find practical guidance on both the technical aspects of latency optimization and real-world implementation approaches. We begin by explaining latency in LLM applications.

Image and video prompt engineering for Amazon Nova Canvas and Amazon Nova Reel

Amazon has introduced two new creative content generation models on Amazon Bedrock: Amazon Nova Canvas for image generation and Amazon Nova Reel for video creation. These models transform text and image inputs into custom visuals, opening up creative opportunities for both professional and personal projects. Nova Canvas, a state-of-the-art image generation model, creates professional-grade images […]