AWS Machine Learning Blog

MARCH 11, 2025

The growing need for cost-effective AI models The landscape of generative AI is rapidly evolving. Although GPT-4o has gained traction in the AI community, enterprises are showing increased interest in Amazon Nova due to its lower latency and cost-effectiveness. Each provisioned node was r7g.4xlarge,

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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. k-NN works by finding the k most similar vectors to a given vector.

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Towards AI

MAY 1, 2024

Last Updated on May 1, 2024 by Editorial Team Author(s): Stephen Chege-Tierra Insights Originally published on Towards AI. We shall look at various types of machine learning algorithms such as decision trees, random forest, K nearest neighbor, and naïve Bayes and how you can call their libraries in R studios, including executing the code.

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Pickl AI

JANUARY 21, 2025

k-Nearest Neighbors (k-NN) k-NN is a simple algorithm that classifies new instances based on the majority class among its k nearest neighbours in the training dataset. K-Means Clustering K-means clustering partitions data into k distinct clusters based on feature similarity.

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Towards AI

APRIL 4, 2024

Last Updated on April 4, 2024 by Editorial Team Author(s): Stephen Chege-Tierra Insights Originally published on Towards AI. Created by the author with DALL E-3 Machine learning algorithms are the “cool kids” of the tech industry; everyone is talking about them as if they were the newest, greatest meme.

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Towards AI

APRIL 7, 2024

Author(s): Stephen Chege-Tierra Insights Originally published on Towards AI. Created by the author with DALL E-3 Statistics, regression model, algorithm validation, Random Forest, K Nearest Neighbors and Naïve Bayes— what in God’s name do all these complicated concepts have to do with you as a simple GIS analyst?

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AWS Machine Learning Blog

NOVEMBER 13, 2024

In the context of generative AI , significant progress has been made in developing multimodal embedding models that can embed various data modalities—such as text, image, video, and audio data—into a shared vector space. He is particularly passionate about AI/ML and enjoys building proof-of-concept solutions for his customers.

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AWS Machine Learning Blog

FEBRUARY 5, 2025

OpenSearchs vector capabilities help accelerate AI application development, making it easier for teams to operationalize, manage, and integrate AI-driven assets. 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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Flipboard

MAY 5, 2025

Amazon OpenSearch Service is a fully managed solution that simplifies the deployment, operation, and scaling of OpenSearch clusters in the AWS Cloud. Figure 2 : Amazon OpenSearch Service for Vector Search: Demo Key Features of AWS OpenSearch Scalability: Easily scale clusters up or down based on workload demands.

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Pickl AI

APRIL 13, 2025

Examples include: Classifying species of plants Categorizing images into animals, vehicles, or landscapes Algorithms like Random Forests, Naive Bayes, and K-Nearest Neighbors (KNN) are commonly used for multi-class classification. Each instance is assigned to one of several predefined categories.

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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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Dataconomy

APRIL 4, 2023

LaMDA, GPT, and more… Nowadays, everyone talking about AI models and what they are capable of. The use of AI models is expanding rapidly across all industries. AI’s capacity to find solutions to difficult issues with minimal human input is a major selling point for the technology. What is an AI model?

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Dataconomy

APRIL 4, 2023

LaMDA, GPT, and more… Nowadays, everyone talking about AI models and what they are capable of. The use of AI models is expanding rapidly across all industries. AI’s capacity to find solutions to difficult issues with minimal human input is a major selling point for the technology. What is an AI model?

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Towards AI

APRIL 14, 2023

Last Updated on April 17, 2023 by Editorial Team Author(s): Kevin Berlemont, PhD Originally published on Towards AI. The prediction is then done using a k-nearest neighbor method within the embedding space. The feature space reduction is performed by aggregating clusters of features of balanced size.

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Flipboard

JUNE 6, 2025

We introduce some use case-specific methods, such as temporal frame smoothing and clustering, to enhance the video search performance. Setting the search size, which can be effectively combined with temporal clustering. The retrieved frame embeddings undergo temporal clustering.

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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. There are different kinds of unsupervised learning algorithms, including clustering, anomaly detection, neural networks, etc. It can be either agglomerative or divisive.

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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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IBM Journey to AI blog

DECEMBER 19, 2023

Common machine learning algorithms for supervised learning include: K-nearest neighbor (KNN) algorithm : This algorithm is a density-based classifier or regression modeling tool used for anomaly detection. “Means,” or average data, refers to the points in the center of the cluster that all other data is related to.

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Mlearning.ai

FEBRUARY 17, 2023

But I also want truly define that ML isn’t represent some kind of unsecured AI technologies, super brain or dark magic, it’s clear combination of programming skills, enough amount of data, cloud solutions, theory of algorithms and math  — that’s all we should have to be able to work in this branch. In this article, I will cover all of them.

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Snorkel AI

APRIL 11, 2023

Cody Coleman, CEO and co-founder of Coactive AI gave a presentation entitled “Data Selection for Data-Centric AI: Quality over Quantity” at Snorkel AI’s Future of Data-Centric AI Event in August 2022. What this means is, in practice when we ran this method, we needed a cluster of machines in order to do a single pass.

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Snorkel AI

APRIL 11, 2023

Cody Coleman, CEO and co-founder of Coactive AI gave a presentation entitled “Data Selection for Data-Centric AI: Quality over Quantity” at Snorkel AI’s Future of Data-Centric AI Event in August 2022. What this means is, in practice when we ran this method, we needed a cluster of machines in order to do a single pass.

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Snorkel AI

APRIL 11, 2023

Cody Coleman, CEO and co-founder of Coactive AI gave a presentation entitled “Data Selection for Data-Centric AI: Quality over Quantity” at Snorkel AI’s Future of Data-Centric AI Event in August 2022. What this means is, in practice when we ran this method, we needed a cluster of machines in order to do a single pass.

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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. With AI and Large Language Models (LLMs) taking over the world (hopefully not like Skynet 🤖), we need smarter ways to store and retrieve high-dimensional data. Traditional databases? They tap out. 💡 Why?

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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. -p

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AWS Machine Learning Blog

APRIL 19, 2024

This solution includes the following components: Amazon Titan Text Embeddings is a text embeddings model that converts natural language text, including single words, phrases, or even large documents, into numerical representations that can be used to power use cases such as search, personalization, and clustering based on semantic similarity.

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AWS Machine Learning Blog

APRIL 5, 2023

out" embeddings.append(json.load(open(embedding_file))[0]) Create an ML-powered unified search engine This section discusses how to create a search engine that that uses k-NN search with embeddings. This includes configuring an OpenSearch Service cluster, ingesting item embedding, and performing free text and image search queries.

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Pickl AI

AUGUST 22, 2023

This harmonization is particularly critical in algorithms such as k-Nearest Neighbors and Support Vector Machines, where distances dictate decisions. Scaling steps in as a guardian, harmonizing the scales and ensuring that algorithms treat each feature fairly.

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AWS Machine Learning Blog

JANUARY 6, 2023

OpenSearch Service currently has tens of thousands of active customers with hundreds of thousands of clusters under management processing trillions of requests per month. OpenSearch Service offers the latest versions of OpenSearch, support for 19 versions of Elasticsearch (1.5 Solution overview. Prerequisites.

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Pickl AI

SEPTEMBER 3, 2023

In 2023, the expected reach of the AI market is supposed to reach the $500 billion mark and in 2030 it is supposed to reach $1,597.1 49% of companies in the world that use Machine Learning and AI in their marketing and sales processes apply it to identify the prospects of sales.

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Snorkel AI

JANUARY 26, 2023

Snorkel AI co-founder and CEO Alex Ratner recently interviewed several Snorkel researchers about their published academic papers. 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.

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Snorkel AI

JANUARY 26, 2023

Snorkel AI co-founder and CEO Alex Ratner recently interviewed several Snorkel researchers about their published academic papers. 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.

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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 example provides a solution for enterprises looking to enhance their AI capabilities. To learn more about deploying DeepSeek-R1 on SageMaker, refer to Deploying DeepSeek-R1 Distill Model on AWS using Amazon SageMaker AI.

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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. Clustering and dimensionality reduction are common tasks in unSupervised Learning. Random Forests).

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Mlearning.ai

JULY 6, 2023

The sub-categories of this approach are negative sampling, clustering, knowledge distillation, and redundancy reduction. Some common quantitative evaluations are linear probing , K nearest neighbors (KNN), and fine-tuning. D BECOME a WRITER at MLearning.ai // invisible ML // Detect AI img Mlearning.ai

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Mlearning.ai

MAY 21, 2023

How to perform Face Recognition using KNN So in this blog, we will see how we can perform Face Recognition using KNN (K-Nearest Neighbors Algorithm) and Haar cascades. Checkout the code walkthrough [link] 13. Haar cascades are very fast as compared to other ways of detecting faces (like MTCNN) but with an accuracy tradeoff.

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Pickl AI

SEPTEMBER 12, 2024

Artificial Intelligence (AI): A branch of computer science focused on creating systems that can perform tasks typically requiring human intelligence. Clustering: An unsupervised Machine Learning technique that groups similar data points based on their inherent similarities.

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The MLOps Blog

DECEMBER 19, 2022

A set of classes sometimes forms a group/cluster. So, we can plot the high-dimensional vector space into lower dimensions and evaluate the integrity at the cluster level. index.add(xb) # xq are query vectors, for which we need to search in xb to find the k nearest neighbors. # Creating the index.

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