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Build a contextual text and image search engine for product recommendations using Amazon Bedrock and Amazon OpenSearch Serverless

AWS Machine Learning - AI

In this post, we show how to build a contextual text and image search engine for product recommendations using the Amazon Titan Multimodal Embeddings model , available in Amazon Bedrock , with Amazon OpenSearch Serverless. Store embeddings into the Amazon OpenSearch Serverless as the search engine.

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

Confluent

With serverless being all the rage, it brings with it a tidal change of innovation. or invest in a vendor-agnostic layer like the serverless framework ? or invest in a vendor-agnostic layer like the serverless framework ? What is more, as the world adopts the event-driven streaming architecture, how does it fit with serverless?

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Multi-LLM routing strategies for generative AI applications on AWS

AWS Machine Learning - AI

For example, a marketing content creation application might need to perform task types such as text generation, text summarization, sentiment analysis, and information extraction as part of producing high-quality, personalized content. An example is a virtual assistant for enterprise business operations.

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Making Apache Kafka Serveless: Lessons From Confluent Cloud

Confluent

Serverless offerings in the cloud are a favorite among software engineers—a prime example are object stores such as AWS S3. For the system designer, however, it is an engineering challenge […].

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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

Amazon Bedrock offers a serverless experience so you can get started quickly, privately customize FMs with your own data, and integrate and deploy them into your applications using AWS tools without having to manage infrastructure. When the deployment is successful (which may take 7–10 minutes to complete), you can start testing the solution.

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Ground truth generation and review best practices for evaluating generative AI question-answering with FMEval

AWS Machine Learning - AI

For example, if ground truth is generated by LLMs before the involvement of SMEs, SMEs will still be needed to identify which questions are fundamental to the business and then align the ground truth with business value as part of a human-in-the-loop process. For our example, we work with Anthropics Claude LLM on Amazon Bedrock.

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Build an end-to-end RAG solution using Knowledge Bases for Amazon Bedrock and AWS CloudFormation

AWS Machine Learning - AI

Prerequisites To implement the solution provided in this post, you should have the following: An active AWS account and familiarity with FMs, Amazon Bedrock, and OpenSearch Serverless. He specializes in generative AI, machine learning, and system design. An S3 bucket where your documents are stored in a supported format (.txt,md,html,doc/docx,csv,xls/.xlsx,pdf).