Generative AI

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Best practices for Meta Llama 3.2 multimodal fine-tuning on Amazon Bedrock

Amazon Bedrock, Best Practices, Foundation models, Generative AI /

Multimodal fine-tuning represents a powerful approach for customizing foundation models (FMs) to excel at specific tasks that involve both visual and textual information. Although base multimodal models offer impressive general capabilities, they often fall short when faced with specialized visual tasks, domain-specific content, or particular output formatting requirements. Fine-tuning addresses these limitations by adapting models […]

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

Amazon Bedrock, Foundation models, Generative AI, Launch /

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 models (students), creating specialized models that

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FloQast builds an AI-powered accounting transformation solution with Anthropic’s Claude 3 on Amazon Bedrock

Amazon Bedrock, Amazon Bedrock Agents, Customer Solutions, Generative AI /

With the advent of generative AI solutions, a paradigm shift is underway across industries, driven by organizations embracing foundation models (FMs) to unlock unprecedented opportunities. Amazon Bedrock has emerged as the preferred choice for numerous customers seeking to innovate and launch generative AI applications, leading to an exponential surge in demand for model inference capabilities.

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Insights in implementing production-ready solutions with generative AI

Artificial Intelligence, AWS Well-Architected Framework, Best Practices, Customer Solutions, Experience-Based Acceleration, Generative AI, Thought Leadership /

As generative AI revolutionizes industries, organizations are eager to harness its potential. However, the journey from production-ready solutions to full-scale implementation can present distinct operational and technical considerations. This post explores key insights and lessons learned from AWS customers in Europe, Middle East, and Africa (EMEA) who have successfully navigated this transition, providing a roadmap

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

Advanced (300), AI/ML, AIML, Amazon Bedrock, Amazon Machine Learning, Amazon SageMaker, Amazon SageMaker Data & AI Governance, Generative AI, Responsible AI /

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 key focus for the industry over

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Improve Amazon Nova migration performance with data-aware prompt optimization

Amazon Bedrock, Amazon Nova, Generative AI, Thought Leadership /

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 teams in Amazon, have started transitioning

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Customize Amazon Nova models to improve tool usage

Amazon Bedrock, Amazon Nova, Fine Tuning, Generative AI, Technical How-to /

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 plan, execute, and refine tasks. Accurate

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Enterprise-grade natural language to SQL generation using LLMs: Balancing accuracy, latency, and scale

Amazon Bedrock, Artificial Intelligence, Generative AI, Technical How-to /

This blog post is co-written with Renuka Kumar and Thomas Matthew from Cisco. Enterprise data by its very nature spans diverse data domains, such as security, finance, product, and HR. Data across these domains is often maintained across disparate data environments (such as Amazon Aurora, Oracle, and Teradata), with each managing hundreds or perhaps thousands

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

Amazon Bedrock, Amazon Nova, Customer Solutions, Foundation models, Generative AI /

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 functions. Their approach emphasizes operational efficiency

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Build an AI-powered document processing platform with open source NER model and LLM on Amazon SageMaker

AI/ML, Amazon Machine Learning, Amazon SageMaker, Amazon SageMaker AI, Artificial Intelligence, Customer Solutions, Generative AI, Natural Language Processing, Technical How-to /

Archival data in research institutions and national laboratories represents a vast repository of historical knowledge, yet much of it remains inaccessible due to factors like limited metadata and inconsistent labeling. Traditional keyword-based search mechanisms are often insufficient for locating relevant documents efficiently, requiring extensive manual review to extract meaningful insights. To address these challenges, a

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