AWS Machine Learning Blog

FEBRUARY 13, 2025

Amazon Bedrock is a fully managed service that makes foundation models (FMs) from leading AI startups and Amazon available through an API, so you can choose from a wide range of FMs to find the model that is best suited for your use case. Caching is performed on Amazon CloudFront for certain topics to ease the database load.

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Flipboard

NOVEMBER 17, 2023

Generative AI models have the potential to revolutionize enterprise operations, but businesses must carefully consider how to harness their power while overcoming challenges such as safeguarding data and ensuring the quality of AI-generated content. Set up the database access and network access.

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AWS Machine Learning Blog

FEBRUARY 5, 2025

These databases typically use k-nearest (k-NN) indexes built with advanced algorithms such as Hierarchical Navigable Small Worlds (HNSW) and Inverted File (IVF) systems. OpenSearch Service then uses the vectors to find the k-nearest neighbors (KNN) to the vectorized search term and image to retrieve the relevant listings.

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Towards AI

MAY 10, 2024

Last Updated on May 13, 2024 by Editorial Team Author(s): Cristian Rodríguez Originally published on Towards AI. 4] Dataset The dataset comes from Kaggle [5], which contains a database of 3206 brain MRI images. The three weak learner models used for this implementation were k-nearest neighbors, decision trees, and naive Bayes.

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Towards AI

FEBRUARY 19, 2025

Last Updated on February 20, 2025 by Editorial Team Author(s): Afaque Umer Originally published on Towards AI. Vector Databases 101: A Beginners Guide to Vector Search and Indexing Photo by Google DeepMind on Unsplash Introduction Alright, folks! Traditional databases? 😎🔥 Section 1: What is a Vector Database?

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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. Archana is an aspiring member of the AI/ML technical field community at AWS.

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AWS Machine Learning Blog

APRIL 3, 2024

Search engines and recommendation systems powered by generative AI can improve the product search experience exponentially by understanding natural language queries and returning more accurate results. With Amazon Titan Multimodal Embeddings, you can generate embeddings for your content and store them in a vector database.

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K-nearest Neighbors AWS Machine Learning Machine Learning 137

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. Prospecting, opportunity progression, and customer engagement present exciting opportunities to utilize generative AI, using historical data, to drive efficiency and effectiveness.

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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.

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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).

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Flipboard

FEBRUARY 7, 2025

DeepSeek-R1 is a powerful and cost-effective AI model that excels at complex reasoning tasks. This post shows you how to set up RAG using DeepSeek-R1 on Amazon SageMaker with an OpenSearch Service vector database as the knowledge base. This example provides a solution for enterprises looking to enhance their AI capabilities.

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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.

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AWS Machine Learning Blog

FEBRUARY 25, 2025

AI now plays a pivotal role in the development and evolution of the automotive sector, in which Applus+ IDIADA operates. In this post, we showcase the research process undertaken to develop a classifier for human interactions in this AI-based environment using Amazon Bedrock.

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Towards AI

APRIL 23, 2025

Last Updated on April 24, 2025 by Editorial Team Author(s): James Luan Originally published on Towards AI. The general perception is that you can simply feed data into an embedding model to generate vector embeddings and then transfer these vectors into your vector database to retrieve the desired results.

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AWS Machine Learning Blog

JANUARY 30, 2024

With the advent of generative AI, today’s foundation models (FMs), such as the large language models (LLMs) Claude 2 and Llama 2, can perform a range of generative tasks such as question answering, summarization, and content creation on text data. Setting k=1 retrieves the most relevant slide to the user question.

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IBM Journey to AI blog

DECEMBER 20, 2023

That’s why diversifying enterprise AI and ML usage can prove invaluable to maintaining a competitive edge. ML is a computer science, data science and artificial intelligence (AI) subset that enables systems to learn and improve from data without additional programming interventions. What is machine learning?

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AWS Machine Learning Blog

DECEMBER 6, 2023

Conversational AI has come a long way in recent years thanks to the rapid developments in generative AI, especially the performance improvements of large language models (LLMs) introduced by training techniques such as instruction fine-tuning and reinforcement learning from human feedback.

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Pickl AI

JULY 17, 2023

Machine Learning is a subset of artificial intelligence (AI) that focuses on developing models and algorithms that train the machine to think and work like a human. It aims to partition a given dataset into K clusters, where each data point belongs to the cluster with the nearest mean.

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AWS Machine Learning Blog

JUNE 3, 2024

Amazon Bedrock is a fully managed service that provides access to a range of high-performing foundation models from leading AI companies through a single API. It offers the capabilities needed to build generative AI applications with security, privacy, and responsible AI. Victor Wang is a Sr.

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PyImageSearch

MAY 12, 2025

For example: Traditional Search: "A superhero film with an AI-powered villain" Doesnt match Avengers: Age of Ultron unless those exact words appear in the dataset. Semantic Search: "A superhero film with an AI-powered villain" Correctly retrieves Avengers: Age of Ultron , even if the description is phrased differently.

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Flipboard

MAY 5, 2025

In this series, we will set up AWS OpenSearch , which will serve as a vector database for a semantic search application that well develop step by step. Hybrid Search: Combines BM25 (Best Match 25) keyword search with vector embeddings, balancing traditional and AI-powered search for precise, relevant results.

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AWS Machine Learning Blog

JANUARY 6, 2023

In this post, we present a solution to handle OOC situations through knowledge graph-based embedding search using the k-nearest neighbor (kNN) search capabilities of OpenSearch Service. Solution overview. The key AWS services used to implement this solution are OpenSearch Service, SageMaker, Lambda, and Amazon S3.

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AWS Machine Learning Blog

SEPTEMBER 25, 2024

Generative AI offers new possibilities to address this challenge and can be used by content teams and influencers to enhance their creativity and engagement while maintaining brand consistency. find_similar_items performs semantic search using the k-nearest neighbors (kNN) algorithm on the input image prompt.

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Flipboard

JUNE 6, 2025

Furthermore, we demonstrate the end-to-end functionality of this approach by using both asynchronous and real-time hosting options on Amazon SageMaker AI to perform video, image, and text processing using publicly available LVMs on the Hugging Face Model Hub. The retrieved frame embeddings undergo temporal clustering.

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Mlearning.ai

NOVEMBER 29, 2023

K-Nearest Neighbor Regression Neural Network (KNN) The k-nearest neighbor (k-NN) algorithm is one of the most popular non-parametric approaches used for classification, and it has been extended to regression. Decision Trees ML-based decision trees are used to classify items (products) in the database.

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Snorkel AI

APRIL 28, 2023

He presented “Building Machine Learning Systems for the Era of Data-Centric AI” at Snorkel AI’s The Future of Data-Centric AI event in 2022. This talk was followed by an audience Q&A conducted by Snorkel AI’s Priyal Aggarwal. Ce Zhang is an associate professor in Computer Science at ETH Zürich.

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Snorkel AI

APRIL 28, 2023

He presented “Building Machine Learning Systems for the Era of Data-Centric AI” at Snorkel AI’s The Future of Data-Centric AI event in 2022. This talk was followed by an audience Q&A conducted by Snorkel AI’s Priyal Aggarwal. Ce Zhang is an associate professor in Computer Science at ETH Zürich.

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ML ML Machine Learning Machine Learning 52

Pickl AI

NOVEMBER 18, 2024

Basics of Machine Learning Machine Learning is a subset of Artificial Intelligence (AI) that allows systems to learn from data, improve from experience, and make predictions or decisions without being explicitly programmed. This data can come from databases, APIs, or public datasets. Random Forests).

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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.

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The MLOps Blog

DECEMBER 19, 2022

HNSW is one of the most straightforward approaches to building a graph for nearest neighbour search, but it’s the best indexing scheme in terms of memory utilisation. Adding vectors to the index (xb are database vectors that are to be indexed). D, I = index.search(xq, k) #Source: [link] Check this out to learn more.

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