Remove Scalability Remove Serverless Remove System Design
article thumbnail

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?

article thumbnail

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.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

Multi-LLM routing strategies for generative AI applications on AWS

AWS Machine Learning - AI

For instance, consider an AI-driven legal document analysis system designed for businesses of varying sizes, offering two primary subscription tiers: Basic and Pro. This hybrid approach combines the scalability and flexibility of semantic search with the precision and context-awareness of classifier LLMs.

article thumbnail

Accelerating insurance policy reviews with generative AI: Verisk’s Mozart companion

AWS Machine Learning - AI

During the solution design process, Verisk also considered using Amazon Bedrock Knowledge Bases because its purpose built for creating and storing embeddings within Amazon OpenSearch Serverless. In the future, Verisk intends to use the Amazon Titan Embeddings V2 model. The user can pick the two documents that they want to compare.

article thumbnail

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).

article thumbnail

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

AWS Machine Learning - AI

By using the AWS CDK, the solution sets up the necessary resources, including an AWS Identity and Access Management (IAM) role, Amazon OpenSearch Serverless collection and index, and knowledge base with its associated data source. He specializes in generative AI, machine learning, and system design.

article thumbnail

High-performance computing on AWS

Xebia

It provides a powerful and scalable platform for executing large-scale batch jobs with minimal setup and management overhead. Scalability: With AWS ParallelCluster, you can easily scale your clusters up or down based on workload demands. This is a serverless web UI that mirrors the pcluster functionality.

AWS 147