Official Machine Learning Blog of Amazon Web Services
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Whereas traditional forecasting methods typically rely on statistical modeling, Chronos treats time series data as a language to be modeled and uses a pre-trained FM to generate forecasts — similar to how large language models (LLMs) generate texts. Chronos helps you achieve accurate predictions faster, significantly reducing development time compared to traditional methods. In this post, we...
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Now with Amazon Bedrock, you can develop custom evaluation metrics for both model and RAG evaluations. This capability extends the LLM-as-a-judge framework that drives Amazon Bedrock Evaluations. In this post, we demonstrate how to use custom metrics in Amazon Bedrock Evaluations to measure and improve the performance of your generative AI applications according to your specific business...
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This post explores a financial assistant system that specializes in three key tasks: portfolio creation, company research, and communication. This post aims to illustrate the use of multiple specialized agents within the Amazon Bedrock multi-agent collaboration capability, with particular emphasis on their application in financial analysis.
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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 the use of AWS generative AI technology.
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In this post, we show how to create an application using Amazon Q Business with Jira integration that used a dataset containing a Trusted Advisor detailed report. This solution demonstrates how to use new generative AI services like Amazon Q Business to get data insights faster and make them actionable.
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In this post, we share comprehensive best practices and scientific insights for fine-tuning Meta Llama 3.2 multimodal models on Amazon Bedrock. By following these guidelines, you can fine-tune smaller, more cost-effective models to achieve performance that rivals or even surpasses much larger models—potentially reducing both inference costs and latency, while maintaining high accuracy for your...
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The MCP proposed by Anthropic offers a standardized way of connecting FMs to data sources, and now you can use this capability with SageMaker AI. In this post, we presented an example of combining the power of SageMaker AI and MCP to build an application that offers a new perspective on loan underwriting through specialized roles and automated workflows.
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In this post, we show how you can automate language localization through translating documents using Amazon Web Services (AWS). The solution combines Amazon Bedrock and AWS Serverless technologies, a suite of fully managed event-driven services for running code, managing data, and integrating applications—all without managing servers.
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In this post, we introduce agentic automatic mortgage approval, a next-generation sample solution that uses autonomous AI agents powered by Amazon Bedrock Agents and Amazon Bedrock Data Automation. These agents orchestrate the entire mortgage approval process—intelligently verifying documents, assessing risk, and making data-driven decisions with minimal human intervention.
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In this post, we highlight the advanced data augmentation techniques and performance improvements in Amazon Bedrock Model Distillation with Meta's Llama model family. 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 excel at specific tasks.