Remove Artificial Intelligence Remove Serverless Remove Storage
article thumbnail

Deploy DeepSeek-R1 Distilled Llama models in Amazon Bedrock

AWS Machine Learning - AI

Amazon Bedrock Custom Model Import enables the import and use of your customized models alongside existing FMs through a single serverless, unified API. This serverless approach eliminates the need for infrastructure management while providing enterprise-grade security and scalability. An S3 bucket prepared to store the custom model.

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.

Insiders

Sign Up for our Newsletter

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

article thumbnail

Use LangChain with PySpark to process documents at massive scale with Amazon SageMaker Studio and Amazon EMR Serverless

AWS Machine Learning - AI

From deriving insights to powering generative artificial intelligence (AI) -driven applications, the ability to efficiently process and analyze large datasets is a vital capability. That’s where the new Amazon EMR Serverless application integration in Amazon SageMaker Studio can help.

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. Review and prepare the dataset.

article thumbnail

4 paths to sustainable AI

CIO

Some hyperscalers offer tools and advice on making AI more sustainable, such as Amazon Web Services, which provides tips on using serverless technologies to eliminate idle resources, data management tools, and datasets. AWS also has models to reduce data processing and storage, and tools to “right size” infrastructure for AI application.

article thumbnail

Accelerate AWS Well-Architected reviews with Generative AI

AWS Machine Learning - AI

The workflow consists of the following steps: WAFR guidance documents are uploaded to a bucket in Amazon Simple Storage Service (Amazon S3). Using Amazon Bedrock Knowledge Base, the sample solution ingests these documents and generates embeddings, which are then stored and indexed in Amazon OpenSearch Serverless.

article thumbnail

Build a contextual chatbot for financial services using Amazon SageMaker JumpStart, Llama 2 and Amazon OpenSearch Serverless with Vector Engine

AWS Machine Learning - AI

We also use Vector Engine for Amazon OpenSearch Serverless (currently in preview) as the vector data store to store embeddings. Use OpenSearch Serverless with the vector engine feature to search for the top K most relevant document indexes in the embedding space. An OpenSearch Serverless collection.