Computer Science, K-nearest Neighbors and ML

OpenSearch Vector Engine is now disk-optimized for low cost, accurate vector search

Flipboard

JANUARY 24, 2025

Overview of vector search and the OpenSearch Vector Engine Vector search is a technique that improves search quality by enabling similarity matching on content that has been encoded by machine learning (ML) models into vectors (numerical encodings). These benchmarks arent designed for evaluating ML models.

K-nearest Neighbors

K-nearest Neighbors ML ML Algorithm

Build cost-effective RAG applications with Binary Embeddings in Amazon Titan Text Embeddings V2, Amazon OpenSearch Serverless, and Amazon Bedrock Knowledge Bases

AWS Machine Learning Blog

NOVEMBER 18, 2024

Amazon OpenSearch Serverless is a serverless deployment option for Amazon OpenSearch Service, a fully managed service that makes it simple to perform interactive log analytics, real-time application monitoring, website search, and vector search with its k-nearest neighbor (kNN) plugin.

K-nearest Neighbors

K-nearest Neighbors AWS ML ML

Build a Search Engine: Semantic Search System Using OpenSearch

PyImageSearch

MAY 19, 2025

In this tutorial, well explore how OpenSearch performs k-NN (k-Nearest Neighbor) search on embeddings. How OpenSearch Uses Neural Search and k-NN Indexing Figure 6 illustrates the entire workflow of how OpenSearch processes a neural query and retrieves results using k-Nearest Neighbor (k-NN) search.

K-nearest Neighbors

K-nearest Neighbors AWS Deep Learning Deep Learning

Webinars

What’s New in Apache Airflow® 3.0—And How Will It Reshape Your Data Workflows?

MORE WEBINARS

Fast-track graph ML with GraphStorm: A new way to solve problems on enterprise-scale graphs

AWS Machine Learning Blog

JUNE 9, 2023

a low-code enterprise graph machine learning (ML) framework to build, train, and deploy graph ML solutions on complex enterprise-scale graphs in days instead of months. With GraphStorm, we release the tools that Amazon uses internally to bring large-scale graph ML solutions to production. license on GitHub. GraphStorm 0.1

ML

ML ML K-nearest Neighbors Machine Learning

From Pixels to Places: Harnessing Geospatial Data with Machine Learning.

Towards AI

APRIL 4, 2024

Machine learning (ML) has proven that it is here with us for the long haul, everyone who had their doubts by calling it a phase should by now realize how wrong they are, ML has being used in various sector’s of society such as medicine, geospatial data, finance, statistics and robotics.

K-nearest Neighbors

K-nearest Neighbors Machine Learning Machine Learning Decision Trees

Five machine learning types to know

IBM Journey to AI blog

DECEMBER 20, 2023

Machine learning (ML) technologies can drive decision-making in virtually all industries, from healthcare to human resources to finance and in myriad use cases, like computer vision , large language models (LLMs), speech recognition, self-driving cars and more. However, the growing influence of ML isn’t without complications.

Machine Learning

Machine Learning Machine Learning Supervised Learning Clustering

Identifying defense coverage schemes in NFL’s Next Gen Stats

AWS Machine Learning Blog

FEBRUARY 10, 2023

Through a collaboration between the Next Gen Stats team and the Amazon ML Solutions Lab , we have developed the machine learning (ML)-powered stat of coverage classification that accurately identifies the defense coverage scheme based on the player tracking data. In this post, we deep dive into the technical details of this ML model.

ML

ML ML Machine Learning Machine Learning

Debugging data to build better and more fair ML applications

Snorkel AI

APRIL 28, 2023

Ce Zhang is an associate professor in Computer Science at ETH Zürich. The talk explored Zhang’s work on how debugging data can lead to more accurate and more fair ML applications. You can approximate your machine learning training components into some simpler classifiers—for example, a k-nearest neighbors classifier.

ML

ML ML Machine Learning Machine Learning

Debugging data to build better and more fair ML applications

Snorkel AI

APRIL 28, 2023

Ce Zhang is an associate professor in Computer Science at ETH Zürich. The talk explored Zhang’s work on how debugging data can lead to more accurate and more fair ML applications. You can approximate your machine learning training components into some simpler classifiers—for example, a k-nearest neighbors classifier.

ML

ML ML Machine Learning Machine Learning

How IDIADA optimized its intelligent chatbot with Amazon Bedrock

AWS Machine Learning Blog

FEBRUARY 25, 2025

Instead of treating each input as entirely unique, we can use a distance-based approach like k-nearest neighbors (k-NN) to assign a class based on the most similar examples surrounding the input. For the classfier, we employed a classic ML algorithm, k-NN, using the scikit-learn Python module.

Algorithm

Algorithm Machine Learning Machine Learning K-nearest Neighbors

Build a Search Engine: Setting Up AWS OpenSearch

Flipboard

MAY 5, 2025

Vector and Semantic Search: Leverages machine learning-powered search techniques, including k-NN (k-nearest neighbors) and dense vector embeddings, for applications like AI-driven search, recommendation systems, and similarity search. Learning to Rank (LTR) and Re-Ranking: Uses ML models (e.g., Thats not the case.

AWS

AWS Clustering Deep Learning Deep Learning

Use DeepSeek with Amazon OpenSearch Service vector database and Amazon SageMaker

Flipboard

FEBRUARY 7, 2025

For more information, see Creating connectors for third-party ML platforms. Create an OpenSearch model When you work with machine learning (ML) models, in OpenSearch, you use OpenSearchs ml-commons plugin to create a model. You created an OpenSearch ML model group and model that you can use to create ingest and search pipelines.

Database

Database AWS Python ML

Build a Search Engine: Deploy Models and Index Data in AWS OpenSearch

PyImageSearch

MAY 12, 2025

Powering Neural Search : Enables advanced similarity-based retrieval using OpenSearchs k-NN (k-Nearest Neighbors) indexing. Registering the Model in OpenSearch We first register the model using OpenSearchs ML Commons API. Or requires a degree in computer science? The following function from utils.py

AWS

AWS K-nearest Neighbors Deep Learning Deep Learning

How Foundation Models bolster programmatic labeling

Snorkel AI

JANUARY 26, 2023

I am a PhD student in the computer science department at Stanford, advised by Chris Ré working on some broad themes of understanding data-centric AI, weak supervision and theoretical machine learning. So, we propose to do this sort of K-nearest-neighbors-type extension per source in the embedding space.

K-nearest Neighbors

K-nearest Neighbors Clustering Computer Science Computer Science

How Foundation Models bolster programmatic labeling

Snorkel AI

JANUARY 26, 2023

I am a PhD student in the computer science department at Stanford, advised by Chris Ré working on some broad themes of understanding data-centric AI, weak supervision and theoretical machine learning. So, we propose to do this sort of K-nearest-neighbors-type extension per source in the embedding space.

K-nearest Neighbors

K-nearest Neighbors Clustering Computer Science Computer Science

[Latest] 20+ Top Machine Learning Projects for final year

Mlearning.ai

MAY 23, 2023

Read the full article here — [link] For final-year students pursuing a degree in computer science or related disciplines, engaging in machine learning projects can be an excellent way to consolidate theoretical knowledge, gain practical experience, and showcase their skills to potential employers.

Machine Learning

Machine Learning Machine Learning K-nearest Neighbors Python

Data Science Current

OpenSearch Vector Engine is now disk-optimized for low cost, accurate vector search

Build cost-effective RAG applications with Binary Embeddings in Amazon Titan Text Embeddings V2, Amazon OpenSearch Serverless, and Amazon Bedrock Knowledge Bases

Webinars

Trending Sources

Build a Search Engine: Semantic Search System Using OpenSearch

Webinars

Fast-track graph ML with GraphStorm: A new way to solve problems on enterprise-scale graphs

From Pixels to Places: Harnessing Geospatial Data with Machine Learning.

Five machine learning types to know

Identifying defense coverage schemes in NFL’s Next Gen Stats

Debugging data to build better and more fair ML applications

Debugging data to build better and more fair ML applications

How IDIADA optimized its intelligent chatbot with Amazon Bedrock

Build a Search Engine: Setting Up AWS OpenSearch

Use DeepSeek with Amazon OpenSearch Service vector database and Amazon SageMaker

Build a Search Engine: Deploy Models and Index Data in AWS OpenSearch

How Foundation Models bolster programmatic labeling

How Foundation Models bolster programmatic labeling

[Latest] 20+ Top Machine Learning Projects for final year

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