Database, K-nearest Neighbors and Natural Language Processing

Database

K-nearest Neighbors

Natural Language Processing

Healthcare revolution: Vector databases for patient similarity search and precision diagnosis

Data Science Dojo

JANUARY 30, 2024

Traditional hea l t h c a r e databases struggle to grasp the complex relationships between patients and their clinical histories. Vector databases are revolutionizing healthcare data management. Unlike traditional, table-like structures, they excel at handling the intricate, multi-dimensional nature of patient information.

Database

Database K-nearest Neighbors Natural Language Processing Algorithm

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

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Webinars

How to Achieve High-Accuracy Results When Using LLMs

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Trending Sources

Vector Databases 101: A Beginner’s Guide to Vector Search and Indexing

Towards AI

FEBRUARY 19, 2025

Vector Databases 101: A Beginners Guide to Vector Search and Indexing Photo by Google DeepMind on Unsplash Introduction Alright, folks! The secret sauce behind all of this is vector search and vector databases, helping power similarity-based recommendations and retrieval! Traditional databases? They tap out.

Database

Database K-nearest Neighbors Machine Learning Machine Learning

Webinars

How to Achieve High-Accuracy Results When Using LLMs

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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. These steps are completed prior to the user interaction steps.

K-nearest Neighbors

K-nearest Neighbors AWS Machine Learning Machine Learning

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

AWS Machine Learning Blog

SEPTEMBER 8, 2023

You then use Exact k-NN with scoring script so that you can search by two fields: celebrity names and the vector that captured the semantic information of the article. You also generate an embedding of this newly written article, so that you can search OpenSearch Service for the nearest images to the article in this vector space.

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 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. These steps are completed prior to the user interaction steps.

AWS

AWS ML ML Database

Five machine learning types to know

IBM Journey to AI blog

DECEMBER 20, 2023

And retailers frequently leverage data from chatbots and virtual assistants, in concert with ML and natural language processing (NLP) technology, to automate users’ shopping experiences. Classification algorithms include logistic regression, k-nearest neighbors and support vector machines (SVMs), among others.

Machine Learning

Machine Learning Machine Learning Supervised Learning Clustering

A Guide to Unsupervised Machine Learning Models | Types | Applications

Pickl AI

JULY 17, 2023

It aims to partition a given dataset into K clusters, where each data point belongs to the cluster with the nearest mean. K-NN (k nearest neighbors): K-Nearest Neighbors (K-NN) is a simple yet powerful algorithm used for both classification and regression tasks in Machine Learning.

Machine Learning

Machine Learning Machine Learning K-nearest Neighbors Clustering

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

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

AWS Machine Learning Blog

JUNE 3, 2024

You store the embeddings of the video frame as a k-nearest neighbors (k-NN) vector in your OpenSearch Service index with the reference to the video clip and the frame in the S3 bucket itself (Step 3). The following diagram visualizes the semantic search with natural language processing (NLP).

AWS

AWS K-nearest Neighbors ML ML

How IDIADA optimized its intelligent chatbot with Amazon Bedrock

AWS Machine Learning Blog

FEBRUARY 25, 2025

Model invocation We use Anthropics Claude 3 Sonnet model for the natural language processing task. This LLM model has a context window of 200,000 tokens, enabling it to manage different languages and retrieve highly accurate answers. temperature This parameter controls the randomness of the language models output.

Algorithm

Algorithm Machine Learning Machine Learning K-nearest Neighbors

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

AWS Machine Learning Blog

JANUARY 6, 2023

For more information about the natural language understanding-powered search functionalities of OpenSearch Service, refer to Building an NLU-powered search application with Amazon SageMaker and the Amazon OpenSearch Service KNN feature. Solution overview. Matthew Rhodes is a Data Scientist I working in the Amazon ML Solutions Lab.

AWS

AWS ML ML Machine Learning

Basic Data Science Terms Every Data Analyst Should Know

Pickl AI

SEPTEMBER 12, 2024

Key Components of Data Science Data Science consists of several key components that work together to extract meaningful insights from data: Data Collection: This involves gathering relevant data from various sources, such as databases, APIs, and web scraping. Data Cleaning: Raw data often contains errors, inconsistencies, and missing values.

Data Analyst

Data Analyst Data Science Machine Learning Machine Learning

Image Embedding: Benefits, Use Cases, and Best Practices

DagsHub

JUNE 24, 2024

Transformer Models: Originally designed for natural language processing, transformers have been adapted to vision transformers (ViT) and are now used for image analysis. They have proved to yield better results than CNNs in some applications.

Clustering

Clustering Machine Learning Machine Learning K-nearest Neighbors

Understanding and Building Machine Learning Models

Pickl AI

NOVEMBER 18, 2024

Natural language processing ( NLP ) allows machines to understand, interpret, and generate human language, which powers applications like chatbots and voice assistants. Unstructured data includes text, images, or audio, which require more processing to extract usable features. Random Forests).

Machine Learning

Machine Learning Machine Learning Decision Trees Algorithm

Data Science Current

Healthcare revolution: Vector databases for patient similarity search and precision diagnosis

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

Webinars

Trending Sources

Vector Databases 101: A Beginner’s Guide to Vector Search and Indexing

Webinars

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

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 2

Five machine learning types to know

A Guide to Unsupervised Machine Learning Models | Types | Applications

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

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

How IDIADA optimized its intelligent chatbot with Amazon Bedrock

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

Basic Data Science Terms Every Data Analyst Should Know

Image Embedding: Benefits, Use Cases, and Best Practices

Understanding and Building Machine Learning Models

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