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Transforming network operations with AI: How Swisscom built a network assistant using Amazon Bedrock
In this post, we explore how Swisscom developed their Network Assistant. We discuss the initial challenges and how they implemented a solution that delivers measurable benefits. We examine the technical architecture, discuss key learnings, and look at future enhancements that can further transform network operations. …
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In this post, we guide you through the stages of customizing large language models (LLMs) with SageMaker Unified Studio and SageMaker AI, covering the end-to-end process starting from data discovery to fine-tuning FMs with SageMaker AI distributed training, tracking metrics using MLflow, and then deploying models using …
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In this post, we show how to use Amazon OpenSearch Service as a vector store to build an efficient RAG application. Link to article: https://aws.amazon.com/blogs/machine-learning/optimize-rag-in-production-environments-using-amazon-sagemaker-jumpstart-and-amazon-opensearch-service/
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Advancing AI agent governance with Boomi and AWS: A unified approach to observability and compliance
In this post, we share how Boomi partnered with AWS to help enterprises accelerate and scale AI adoption with confidence using Agent Control Tower. Link to article: https://aws.amazon.com/blogs/machine-learning/advancing-ai-agent-governance-with-boomi-and-aws-a-unified-approach-to-observability-and-compliance/
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In this post, we demonstrate how you can use SageMaker Unified Studio to create complex AI workflows using Amazon Bedrock Flows. Link to article: https://aws.amazon.com/blogs/machine-learning/use-amazon-sagemaker-unified-studio-to-build-complex-ai-workflows-using-amazon-bedrock-flows/
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In this post, we present a centralized Model Context Protocol (MCP) server implementation using Amazon Bedrock that provides shared access to tools and resources for enterprise AI workloads. The solution enables organizations to accelerate AI innovation by standardizing access to resources and tools through MCP, while …
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In this post, we explore five different patterns for implementing LLM-powered structured data query capabilities in AWS, including direct conversational interfaces, BI tool enhancements, and custom text-to-SQL solutions. Link to article: …
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In this post, we explore how Amazon Bedrock's multimodal RAG capabilities revolutionize drug data analysis by efficiently processing complex medical documentation containing text, images, graphs, and tables. Link to article: …
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In this post, we provide an overview of the user experience, detailing how to set up and deploy these workflows with multiple models using the SageMaker Python SDK. We walk through examples of building complex inference workflows, deploying them to SageMaker endpoints, and invoking them for real-time inference. Link to …
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In this post, we look at a step-by-step implementation for using the custom document enrichment (CDE) feature within an Amazon Q Business application to process standalone image files. We walk you through an AWS Lambda function configured within CDE to process various image file types, and showcase an example scenario …
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In this post, we explore how to use Amazon Q Developer CLI with the AWS Diagram MCP and the AWS Documentation MCP servers to create sophisticated architecture diagrams that follow AWS best practices. We discuss techniques for basic diagrams and real-world diagrams, with detailed examples and step-by-step instructions. …
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In this post, we explore how to use Amazon Q CLI with the AWS Cost Analysis MCP server to perform sophisticated cost analysis that follows AWS best practices. We discuss basic setup and advanced techniques, with detailed examples and step-by-step instructions. Link to article: …
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