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Amazon Q Business simplifies integration of enterprise knowledge bases at scale

AWS Machine Learning - AI

Solution overview The following architecture diagram represents the high-level design of a solution proven effective in production environments for AWS Support Engineering. The following diagram illustrates an example architecture for ingesting data through an endpoint interfacing with a large corpus.

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Harness the power of MCP servers with Amazon Bedrock Agents

AWS Machine Learning - AI

Using a client-server architecture, MCP enables developers to expose their data through lightweight MCP servers while building AI applications as MCP clients that connect to these servers. In the first flow, a Lambda-based action is taken, and in the second, the agent uses an MCP server.

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Storm in the stratosphere: how the cloud will be reshuffled

Erik Bernhardsson

That nets AWS about 6 $500-700k in gross profits, after paying for EC2 operational cost and depreciation. This isn't exactly a new idea—Heroku launched in 2007, and AWS Lambda in 2014. ↩︎ There was one major architectural difference of Snowflake vs Redshift. Maybe owning the lowest layer isn't so bad?

Cloud 351
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Learning Lambda — Part 8

Mike Roberts

Cold Starts This is Part 8 of Learning Lambda, a tutorial series about engineering using AWS Lambda. In this installment of Learning Lambda I discuss Cold Starts. In this installment of Learning Lambda I discuss Cold Starts. Way back in Part 3 I talked about the lifecycle of a Lambda function.

Lambda 52
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AI-powered assistants for investment research with multi-modal data: An application of Agents for Amazon Bedrock

AWS Machine Learning - AI

In this post, an AI-powered assistant for investment research can use both structured and unstructured data for providing context to the LLM using a Retrieval Augmented Generation (RAG) architecture, as illustrated in the following diagram. The following diagram illustrates the technical architecture. from the 10K report.

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Boosting RAG-based intelligent document assistants using entity extraction, SQL querying, and agents with Amazon Bedrock

AWS Machine Learning - AI

Then we introduce you to a more versatile architecture that overcomes these limitations. We also present a more versatile architecture that overcomes these limitations. In practice, we implemented this solution as outlined in the following detailed architecture.

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Journey to Event Driven – Part 3: The Affinity Between Events, Streams and Serverless

Confluent

Given that it is at a relatively early stage, developers are still trying to grok the best approach for each cloud vendor and often face the following question: Should I go cloud native with AWS Lambda, GCP functions, etc., What is more, as the world adopts the event-driven streaming architecture, how does it fit with serverless?