Amazon Bedrock Model Distillation: Boost function calling accuracy while reducing cost and latency

Favorite Amazon Bedrock Model Distillation is generally available, and it addresses the fundamental challenge many organizations face when deploying generative AI: how to maintain high performance while reducing costs and latency. This technique transfers knowledge from larger, more capable foundation models (FMs) that act as teachers to smaller, more efficient

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Shared by AWS Machine Learning May 1, 2025

Improve Amazon Nova migration performance with data-aware prompt optimization

Favorite In the era of generative AI, new large language models (LLMs) are continually emerging, each with unique capabilities, architectures, and optimizations. Among these, Amazon Nova foundation models (FMs) deliver frontier intelligence and industry-leading cost-performance, available exclusively on Amazon Bedrock. Since its launch in 2024, generative AI practitioners, including the

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Shared by AWS Machine Learning April 30, 2025

InterVision accelerates AI development using AWS LLM League and Amazon SageMaker AI

Favorite Cities and local governments are continuously seeking ways to enhance their non-emergency services, recognizing that intelligent, scalable contact center solutions play a crucial role in improving citizen experiences. InterVision Systems, LLC (InterVision), an AWS Premier Tier Services Partner and Amazon Connect Service Delivery Partner, has been at the forefront

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Shared by AWS Machine Learning April 30, 2025

Responsible AI in action: How Data Reply red teaming supports generative AI safety on AWS

Favorite Generative AI is rapidly reshaping industries worldwide, empowering businesses to deliver exceptional customer experiences, streamline processes, and push innovation at an unprecedented scale. However, amidst the excitement, critical questions around the responsible use and implementation of such powerful technology have started to emerge. Although responsible AI has been a

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Shared by AWS Machine Learning April 30, 2025

Evaluate Amazon Bedrock Agents with Ragas and LLM-as-a-judge

Favorite AI agents are quickly becoming an integral part of customer workflows across industries by automating complex tasks, enhancing decision-making, and streamlining operations. However, the adoption of AI agents in production systems requires scalable evaluation pipelines. Robust agent evaluation enables you to gauge how well an agent is performing certain

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Shared by AWS Machine Learning April 29, 2025

Customize Amazon Nova models to improve tool usage

Favorite Modern large language models (LLMs) excel in language processing but are limited by their static training data. However, as industries require more adaptive, decision-making AI, integrating tools and external APIs has become essential. This has led to the evolution and rapid rise of agentic workflows, where AI systems autonomously

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Shared by AWS Machine Learning April 29, 2025

Combine keyword and semantic search for text and images using Amazon Bedrock and Amazon OpenSearch Service

Favorite Customers today expect to find products quickly and efficiently through intuitive search functionality. A seamless search journey not only enhances the overall user experience, but also directly impacts key business metrics such as conversion rates, average order value, and customer loyalty. According to a McKinsey study, 78% of consumers

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Shared by AWS Machine Learning April 25, 2025

AWS Field Experience reduced cost and delivered low latency and high performance with Amazon Nova Lite foundation model

Favorite AWS Field Experience (AFX) empowers Amazon Web Services (AWS) sales teams with generative AI solutions built on Amazon Bedrock, improving how AWS sellers and customers interact. The AFX team uses AI to automate tasks and provide intelligent insights and recommendations, streamlining workflows for both customer-facing roles and internal support

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Shared by AWS Machine Learning April 25, 2025

Protect sensitive data in RAG applications with Amazon Bedrock

Favorite Retrieval Augmented Generation (RAG) applications have become increasingly popular due to their ability to enhance generative AI tasks with contextually relevant information. Implementing RAG-based applications requires careful attention to security, particularly when handling sensitive data. The protection of personally identifiable information (PII), protected health information (PHI), and confidential business

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Shared by AWS Machine Learning April 24, 2025