AWS, Database and K-nearest Neighbors

AWS

Database

K-nearest Neighbors

Retrieval-Augmented Generation with LangChain, Amazon SageMaker JumpStart, and MongoDB Atlas semantic search

Flipboard

NOVEMBER 17, 2023

The Retrieval-Augmented Generation (RAG) framework augments prompts with external data from multiple sources, such as document repositories, databases, or APIs, to make foundation models effective for domain-specific tasks. Its vector data store seamlessly integrates with operational data storage, eliminating the need for a separate database.

K-nearest Neighbors

K-nearest Neighbors AWS Clustering Database

Build a reverse image search engine with Amazon Titan Multimodal Embeddings in Amazon Bedrock and AWS managed services

AWS Machine Learning Blog

NOVEMBER 13, 2024

It works by analyzing the visual content to find similar images in its database. Store embeddings : Ingest the generated embeddings into an OpenSearch Serverless vector index, which serves as the vector database for the solution. Display results : Display the top K similar results to the user.

AWS

AWS Database K-nearest Neighbors AI

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AWS empowers sales teams using generative AI solution built on Amazon Bedrock

AWS Machine Learning Blog

AUGUST 26, 2024

At AWS, we are transforming our seller and customer journeys by using generative artificial intelligence (AI) across the sales lifecycle. It will be able to answer questions, generate content, and facilitate bidirectional interactions, all while continuously using internal AWS and external data to deliver timely, personalized insights.

AWS

AWS AI AI K-nearest Neighbors

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Talk to your slide deck using multimodal foundation models hosted on Amazon Bedrock and Amazon SageMaker – Part 2

AWS Machine Learning Blog

APRIL 19, 2024

We stored the embeddings in a vector database and then used the Large Language-and-Vision Assistant (LLaVA 1.5-7b) 7b) model to generate text responses to user questions based on the most similar slide retrieved from the vector database. OpenSearch Serverless is an on-demand serverless configuration for Amazon OpenSearch Service.

AWS

AWS ML ML Database

Semantic image search for articles using Amazon Rekognition, Amazon SageMaker foundation models, and Amazon OpenSearch Service

AWS Machine Learning Blog

SEPTEMBER 8, 2023

It also relies on the images in the repository being tagged correctly, which can also be automated (for a customer success story, refer to Aller Media Finds Success with KeyCore and AWS ). In this post, we demonstrate how to use Amazon Rekognition , Amazon SageMaker JumpStart , and Amazon OpenSearch Service to solve this business problem.

K-nearest Neighbors

K-nearest Neighbors AWS ML ML

Talk to your slide deck using multimodal foundation models hosted on Amazon Bedrock and Amazon SageMaker – Part 1

AWS Machine Learning Blog

JANUARY 30, 2024

and AWS services including Amazon Bedrock and Amazon SageMaker to perform similar generative tasks on multimodal data. We use OpenSearch Serverless as a vector database for storing embeddings generated by the Titan Multimodal Embeddings model. Setting k=1 retrieves the most relevant slide to the user question.

AWS

AWS ML ML K-nearest Neighbors

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

AWS Machine Learning Blog

APRIL 3, 2024

We detail the steps to use an Amazon Titan Multimodal Embeddings model to encode images and text into embeddings, ingest embeddings into an OpenSearch Service index, and query the index using the OpenSearch Service k-nearest neighbors (k-NN) functionality. Virginia) and US West (Oregon) AWS Regions.

K-nearest Neighbors

K-nearest Neighbors AWS Machine Learning Machine Learning

Implement serverless semantic search of image and live video with Amazon Titan Multimodal Embeddings

AWS Machine Learning Blog

JUNE 3, 2024

Kinesis Video Streams makes it straightforward to securely stream video from connected devices to AWS for analytics, machine learning (ML), playback, and other processing. This solution was created with AWS Amplify. It enables real-time video ingestion, storage, encoding, and streaming across devices.

AWS

AWS K-nearest Neighbors ML ML

Power recommendations and search using an IMDb knowledge graph – Part 3

AWS Machine Learning Blog

JANUARY 6, 2023

Many AWS media and entertainment customers license IMDb data through AWS Data Exchange to improve content discovery and increase customer engagement and retention. We downloaded the data from AWS Data Exchange and processed it in AWS Glue to generate KG files. Background. Solution overview. Launch solution resources.

AWS

AWS ML ML Machine Learning

Boosting RAG-based intelligent document assistants using entity extraction, SQL querying, and agents with Amazon Bedrock

AWS Machine Learning Blog

DECEMBER 6, 2023

Another driver behind RAG’s popularity is its ease of implementation and the existence of mature vector search solutions, such as those offered by Amazon Kendra (see Amazon Kendra launches Retrieval API ) and Amazon OpenSearch Service (see k-Nearest Neighbor (k-NN) search in Amazon OpenSearch Service ), among others.

SQL

SQL AWS Analytics Analytics

From RAG to fabric: Lessons learned from building real-world RAGs at GenAIIC – Part 2

AWS Machine Learning Blog

NOVEMBER 15, 2024

The available data sources are: Stock Prices Database Contains historical stock price data for publicly traded companies. Analyst Notes Database Knowledge base containing reports from Analysts on their interpretation and analyis of economic events. Stock Prices Database The question is about a stock price.

Database

Database SQL Data Analysis Data Analysis

Talk to your slide deck using multimodal foundation models on Amazon Bedrock – Part 3

AWS Machine Learning Blog

DECEMBER 10, 2024

We stored the embeddings in a vector database and then used the Large Language-and-Vision Assistant (LLaVA 1.5-7b) 7b) model to generate text responses to user questions based on the most similar slide retrieved from the vector database. You can do this by deleting the stacks using the AWS CloudFormation console.

AWS

AWS K-nearest Neighbors Database ML

Build a multimodal social media content generator using Amazon Bedrock

AWS Machine Learning Blog

SEPTEMBER 25, 2024

The whole process is shown in the following image: Implementation steps This solution has been tested in AWS Region us-east-1. To set up a JupyterLab space Sign in to your AWS account and open the AWS Management Console. However, it can also work in other Regions where the following services are available.

AWS

AWS K-nearest Neighbors ML ML

From RAG to fabric: Lessons learned from building real-world RAGs at GenAIIC – Part 1

AWS Machine Learning Blog

OCTOBER 24, 2024

The AWS Generative AI Innovation Center (GenAIIC) is a team of AWS science and strategy experts who have deep knowledge of generative AI. They help AWS customers jumpstart their generative AI journey by building proofs of concept that use generative AI to bring business value. doc,pdf, or.txt).

AWS

AWS K-nearest Neighbors Database AI

Understanding and Building Machine Learning Models

Pickl AI

NOVEMBER 18, 2024

Structured data refers to neatly organised data that fits into tables, such as spreadsheets or databases, where each column represents a feature and each row represents an instance. This data can come from databases, APIs, or public datasets. K-Nearest Neighbors), while others can handle large datasets efficiently (e.g.,

Machine Learning

Machine Learning Machine Learning Decision Trees Algorithm

Data Science Current

Retrieval-Augmented Generation with LangChain, Amazon SageMaker JumpStart, and MongoDB Atlas semantic search

Build a reverse image search engine with Amazon Titan Multimodal Embeddings in Amazon Bedrock and AWS managed services

Webinars

Trending Sources

AWS empowers sales teams using generative AI solution built on Amazon Bedrock

Webinars

Talk to your slide deck using multimodal foundation models hosted on Amazon Bedrock and Amazon SageMaker – Part 2

Semantic image search for articles using Amazon Rekognition, Amazon SageMaker foundation models, and Amazon OpenSearch Service

Talk to your slide deck using multimodal foundation models hosted on Amazon Bedrock and Amazon SageMaker – Part 1

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

Implement serverless semantic search of image and live video with Amazon Titan Multimodal Embeddings

Power recommendations and search using an IMDb knowledge graph – Part 3

Boosting RAG-based intelligent document assistants using entity extraction, SQL querying, and agents with Amazon Bedrock

From RAG to fabric: Lessons learned from building real-world RAGs at GenAIIC – Part 2

Talk to your slide deck using multimodal foundation models on Amazon Bedrock – Part 3

Build a multimodal social media content generator using Amazon Bedrock

From RAG to fabric: Lessons learned from building real-world RAGs at GenAIIC – Part 1

Understanding and Building Machine Learning Models

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