Remove Artificial Inteligence Remove Lambda Remove Machine Learning
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

Techniques and approaches for monitoring large language models on AWS

AWS Machine Learning - AI

Large Language Models (LLMs) have revolutionized the field of natural language processing (NLP), improving tasks such as language translation, text summarization, and sentiment analysis. Monitoring the performance and behavior of LLMs is a critical task for ensuring their safety and effectiveness.

Insiders

Sign Up for our Newsletter

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

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

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

Harness the power of MCP servers with Amazon Bedrock Agents

AWS Machine Learning - AI

AI agents extend large language models (LLMs) by interacting with external systems, executing complex workflows, and maintaining contextual awareness across operations. In the first flow, a Lambda-based action is taken, and in the second, the agent uses an MCP server.

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. You can invoke Lambda functions from over 200 AWS services and software-as-a-service (SaaS) applications.

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. 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.