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Multi-LLM routing strategies for generative AI applications on AWS

AWS Machine Learning - AI

The Pro tier, however, would require a highly customized LLM that has been trained on specific data and terminology, enabling it to assist with intricate tasks like drafting complex legal documents. This architecture workflow includes the following steps: A user submits a question through a web or mobile application. 70B and 8B.

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How BQA streamlines education quality reporting using Amazon Bedrock

AWS Machine Learning - AI

The Education and Training Quality Authority (BQA) plays a critical role in improving the quality of education and training services in the Kingdom Bahrain. BQA oversees a comprehensive quality assurance process, which includes setting performance standards and conducting objective reviews of education and training institutions.

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Creating asynchronous AI agents with Amazon Bedrock

AWS Machine Learning - AI

This post will discuss agentic AI driven architecture and ways of implementing. Agentic AI architecture Agentic AI architecture is a shift in process automation through autonomous agents towards the capabilities of AI, with the purpose of imitating cognitive abilities and enhancing the actions of traditional autonomous agents.

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Build a video insights and summarization engine using generative AI with Amazon Bedrock

AWS Machine Learning - AI

We walk through the key components and services needed to build the end-to-end architecture, offering example code snippets and explanations for each critical element that help achieve the core functionality. You can invoke Lambda functions from over 200 AWS services and software-as-a-service (SaaS) applications.

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Introducing AWS MCP Servers for code assistants (Part 1)

AWS Machine Learning - AI

Through MCP, general-purpose LLMs can now seamlessly access relevant knowledge beyond initial training data and be effectively steered towards desired outputs by incorporating specific context and best practices. Lets create an architecture that uses Amazon Bedrock Agents with a custom action group to call your internal API.

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Automate emails for task management using Amazon Bedrock Agents, Amazon Bedrock Knowledge Bases, and Amazon Bedrock Guardrails

AWS Machine Learning - AI

Key challenges include the need for ongoing training for support staff, difficulties in managing and retrieving scattered information, and maintaining consistency across different agents’ responses. Solution overview This section outlines the architecture designed for an email support system using generative AI.

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Build a multi-tenant generative AI environment for your enterprise on AWS

AWS Machine Learning - AI

It also uses a number of other AWS services such as Amazon API Gateway , AWS Lambda , and Amazon SageMaker. You can also bring your own customized models and deploy them to Amazon Bedrock for supported architectures. Alternatively, you can use AWS Lambda and implement your own logic, or use open source tools such as fmeval.