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​[[{“value”:” The Amazon Bedrock multi-agent collaboration feature gives developers the flexibility to create and coordinate multiple AI agents, each specialized for specific tasks, to work together efficiently on complex business processes. This enables seamless handling of sophisticated workflows through agent cooperation. This post aims to… Read More »Build a gen AI–powered financial assistant with Amazon Bedrock multi-agent collaboration Suheel Farooq AWS Machine Learning Blog

​[[{“value”:” In this post, we showcase how Dr. Kori Ramajoo, Dr. Sonia Brownsett, Prof. David Copland, from QARC, and Scott Harding, a person living with aphasia, used AWS services to develop WordFinder, a mobile, cloud-based solution that helps individuals with aphasia increase their independence through… Read More »WordFinder app: Harnessing generative AI on AWS for aphasia communication Kori Ramijoo, Scott Harding, Sonia Brownsett, David Copland AWS Machine Learning Blog

​[[{“value”:” Our customers’ key strategic objectives are cost savings and building secure and resilient infrastructure. At AWS, we’re dedicated to helping you meet these critical goals with our unparalleled expertise and industry-leading tools. One of the most valuable resources we offer is the AWS Trusted… Read More »Get faster and actionable AWS Trusted Advisor insights to make data-driven decisions using Amazon Q Business Satish Bhonsle AWS Machine Learning Blog

​[[{“value”:” 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… Read More »Best practices for Meta Llama 3.2 multimodal fine-tuning on Amazon Bedrock Yanyan Zhang AWS Machine Learning Blog

​[[{“value”:” Organizations implementing agents and agent-based systems often experience challenges such as implementing multiple tools, function calling, and orchestrating the workflows of the tool calling. An agent uses a function call to invoke an external tool (like an API or database) to perform specific actions… Read More »Extend large language models powered by Amazon SageMaker AI using Model Context Protocol Mona Mona AWS Machine Learning Blog

​[[{“value”:” Multinational organizations face the complex challenge of effectively managing a workforce and operations across different countries, cultures, and languages. Maintaining consistency and alignment across these global operations can be difficult, especially when it comes to updating and sharing business documents and processes. Delays or… Read More »Automate document translation and standardization with Amazon Bedrock and Amazon Translate Nadhya Polanco AWS Machine Learning Blog

​[[{“value”:” Mortgage processing is a complex, document-heavy workflow that demands accuracy, efficiency, and compliance. Traditional mortgage operations rely on manual review, rule-based automation, and disparate systems, often leading to delays, errors, and a poor customer experience. Recent industry surveys indicate that only about half of… Read More »Autonomous mortgage processing using Amazon Bedrock Data Automation and Amazon Bedrock Agents Wrick Talukdar AWS Machine Learning Blog

​[[{“value”:” 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… Read More »Amazon Bedrock Model Distillation: Boost function calling accuracy while reducing cost and latency Yanyan Zhang AWS Machine Learning Blog