Remove Artificial Inteligence Remove Lambda Remove Serverless
article thumbnail

Multi-LLM routing strategies for generative AI applications on AWS

AWS Machine Learning - AI

Organizations are increasingly using multiple large language models (LLMs) when building generative AI applications. Although an individual LLM can be highly capable, it might not optimally address a wide range of use cases or meet diverse performance requirements.

article thumbnail

Build an AI-powered document processing platform with open source NER model and LLM on Amazon SageMaker

AWS Machine Learning - AI

National Laboratory has implemented an AI-driven document processing platform that integrates named entity recognition (NER) and large language models (LLMs) on Amazon SageMaker AI. In this post, we discuss how you can build an AI-powered document processing platform with open source NER and LLMs on SageMaker.

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

Create a generative AI–powered custom Google Chat application using Amazon Bedrock

AWS Machine Learning - AI

The solution integrates large language models (LLMs) with your organization’s data and provides an intelligent chat assistant that understands conversation context and provides relevant, interactive responses directly within the Google Chat interface. Which LLM you want to use in Amazon Bedrock for text generation.

article thumbnail

Revolutionize trip planning with Amazon Bedrock and Amazon Location Service

AWS Machine Learning - AI

If an image is uploaded, it is stored in Amazon Simple Storage Service (Amazon S3) , and a custom AWS Lambda function will use a machine learning model deployed on Amazon SageMaker to analyze the image to extract a list of place names and the similarity score of each place name. Here is an example from LangChain.

article thumbnail

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

AWS Machine Learning - AI

This engine uses artificial intelligence (AI) and machine learning (ML) services and generative AI on AWS to extract transcripts, produce a summary, and provide a sentiment for the call. Organizations typically can’t predict their call patterns, so the solution relies on AWS serverless services to scale during busy times.

article thumbnail

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. API Gateway is serverless and hence automatically scales with traffic. It’s serverless so you don’t have to manage the infrastructure. This implementation overcomes timeout limitations in synchronous REST requests.

article thumbnail

Accelerate AWS Well-Architected reviews with Generative AI

AWS Machine Learning - AI

Customizable Uses prompt engineering , which enables customization and iterative refinement of the prompts used to drive the large language model (LLM), allowing for refining and continuous enhancement of the assessment process. The WAFR reviewer, based on Lambda and AWS Step Functions , is activated by Amazon SQS.