AWS Machine Learning Blog

NOVEMBER 13, 2024

If you haven’t set up a SageMaker Studio domain, see this Amazon SageMaker blog post for instructions on setting up SageMaker Studio for individual users. To search against the database, you can use a vector search, which is performed using the k-nearest neighbors (k-NN) algorithm.

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Pickl AI

JANUARY 21, 2025

This blog explores various types of Machine Learning algorithms, illustrating their functionalities and applications with relevant examples. 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.

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Pickl AI

MARCH 26, 2024

The K Nearest Neighbors (KNN) algorithm of machine learning stands out for its simplicity and effectiveness. This blog aims to familiarise you with the fundamentals of the KNN algorithm in machine learning and its importance in shaping modern data analytics methodologies. What are K Nearest Neighbors in Machine Learning?

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AWS Machine Learning Blog

DECEMBER 10, 2024

We performed a k-nearest neighbor (k-NN) search to retrieve the most relevant embedding matching the question. Archana is an aspiring member of the AI/ML technical field community at AWS. She focuses on providing technical guidance in a variety of technical domains, including AI/ML.

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AWS Machine Learning Blog

SEPTEMBER 8, 2023

Amazon Rekognition makes it easy to add image analysis capability to your applications without any machine learning (ML) expertise and comes with various APIs to fulfil use cases such as object detection, content moderation, face detection and analysis, and text and celebrity recognition, which we use in this example.

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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.

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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. In her free time, she likes to go for long runs along the beach.

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AWS Machine Learning Blog

SEPTEMBER 29, 2023

In this post, we illustrate how to use a segmentation machine learning (ML) model to identify crop and non-crop regions in an image. Identifying crop regions is a core step towards gaining agricultural insights, and the combination of rich geospatial data and ML can lead to insights that drive decisions and actions.

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AWS Machine Learning Blog

SEPTEMBER 6, 2023

A k-Nearest Neighbor (k-NN) index is enabled to allow searching of embeddings from the OpenSearch Service. Shikhar Kwatra is an AI/ML Specialist Solutions Architect at Amazon Web Services, working with a leading Global System Integrator.

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AWS Machine Learning Blog

JANUARY 6, 2023

In Part 2 , we demonstrated how to use Amazon Neptune ML (in Amazon SageMaker ) to train the KG and create KG embeddings. This mapping can be done by manually mapping frequent OOC queries to catalog content or can be automated using machine learning (ML). The IMDb-Knowledge-Graph-Blog/part3-out-of-catalog/run_imdb_demo.py

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AWS Machine Learning Blog

NOVEMBER 30, 2023

The previous post discussed how you can use Amazon machine learning (ML) services to help you find the best images to be placed along an article or TV synopsis without typing in keywords. Amazon Rekognition automatically recognizes tens of thousands of well-known personalities in images and videos using ML.

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AWS Machine Learning Blog

JANUARY 30, 2024

We perform a k-nearest neighbor (k=1) search to retrieve the most relevant embedding matching the user query. Setting k=1 retrieves the most relevant slide to the user question. As per the AI/ML flywheel, what do the AWS AI/ML services provide? get('hits')[0].get('_source').get('image_path')

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Dataconomy

APRIL 4, 2023

Some of the common types are: Linear Regression Deep Neural Networks Logistic Regression Decision Trees AI Linear Discriminant Analysis Naive Bayes Support Vector Machines Learning Vector Quantization K-nearest Neighbors Random Forest What do they mean? Let’s dig deeper and learn more about them!

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Dataconomy

APRIL 4, 2023

Some of the common types are: Linear Regression Deep Neural Networks Logistic Regression Decision Trees AI Linear Discriminant Analysis Naive Bayes Support Vector Machines Learning Vector Quantization K-nearest Neighbors Random Forest What do they mean? Let’s dig deeper and learn more about them!

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AWS Machine Learning Blog

APRIL 19, 2024

We perform a k-nearest neighbor (k-NN) search to retrieve the most relevant embeddings matching the user query. As per the AI/ML flywheel, what do the AWS AI/ML services provide? Based on the summary, the AWS AI/ML services provide a range of capabilities that fuel an AI/ML flywheel.

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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.,

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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. He is passionate about IoT, AI/ML and building smart home devices. It enables real-time video ingestion, storage, encoding, and streaming across devices.

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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.

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Flipboard

JUNE 6, 2025

The query with embeddings is sent to the OpenSearch vector index, which performs a k-nearest neighbors (k-NN) search to retrieve relevant frame embeddings. About the Authors Dr. Alexander Arzhanov is an AI/ML Specialist Solutions Architect based in Frankfurt, Germany.

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AWS Machine Learning Blog

APRIL 5, 2023

Amazon SageMaker Serverless Inference is a purpose-built inference service that makes it easy to deploy and scale machine learning (ML) models. PyTorch is an open-source ML framework that accelerates the path from research prototyping to production deployment. You can use CLIP with Amazon SageMaker to perform encoding.

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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.

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The MLOps Blog

DECEMBER 19, 2022

As Data Scientists, we all have worked on an ML classification model. In this article, we will talk about feasible techniques to deal with such a large-scale ML Classification model. In this article, you will learn: 1 What are some examples of large-scale ML classification models? Let’s take a look at some of them.

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AWS Machine Learning Blog

OCTOBER 24, 2024

Evaluating a RAG solution Contrary to traditional machine learning (ML) models, for which evaluation metrics are well defined and straightforward to compute, evaluating a RAG framework is still an open problem. Practically, this can be achieved in OpenSearch by combining a k-nearest neighbors (k-NN) query with keyword matching.

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AWS Machine Learning Blog

AUGUST 26, 2024

This includes sales collateral, customer engagements, external web data, machine learning (ML) insights, and more. AI-driven recommendations – By combining generative AI with ML, we deliver intelligent suggestions for products, services, applicable use cases, and next steps.

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Heartbeat

SEPTEMBER 22, 2023

K-Nearest Neighbors (KNN) Classifier: The KNN algorithm relies on selecting the right number of neighbors and a power parameter p. Automating Hyperparameter Tuning with Comet ML To streamline the hyperparameter tuning process, tools like Comet ML come into play. Follow “Nhi Yen” for future updates!

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PyImageSearch

MAY 12, 2025

Home Table of Contents Build a Search Engine: Deploy Models and Index Data in AWS OpenSearch Introduction What Will We Do in This Blog? What Will We Do in This Blog? By the end of this guide, you will have a fully indexed movie dataset with embeddings, ready for semantic search in the next blog. What’s Coming Next?

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Mlearning.ai

APRIL 6, 2023

⚠ You can solve the below-mentioned questions from this blog ⚠ ✔ What if I am building Low code — No code ML automation tool and I do not have any orchestrator or memory management system ? ✔ how to reduce the complexity and computational expensiveness of ML models ? will my data help in this ? Let’s create a community!

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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.

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Pickl AI

JULY 17, 2023

The following blog will focus on Unsupervised Machine Learning Models focusing on the algorithms and types with examples. It aims to partition a given dataset into K clusters, where each data point belongs to the cluster with the nearest mean. Hence, it is considered as one of the best-unsupervised learning algorithms.

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Google Research AI blog

AUGUST 15, 2023

Effective recommendations that present students with relevant reading material helps keep students reading, and this is where machine learning (ML) can help. ML has been widely used in building recommender systems for various types of digital content, ranging from videos to books to e-commerce items.

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AWS Machine Learning Blog

SEPTEMBER 25, 2024

find_similar_items performs semantic search using the k-nearest neighbors (kNN) algorithm on the input image prompt. See this announcement blog post for information about the Amazon Titan Image Generator and Amazon Titan Multimodal Embeddings model. The following code snippet shows the implementation of this step.

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ODSC - Open Data Science

JANUARY 5, 2024

In this blog, we’re going to take a look at some of the top Python libraries of 2023 and see what exactly makes them tick. PyTorch This essential library is an open-source ML framework capable of speeding up research prototyping, allowing companies to enter the production deployment phase.

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Mlearning.ai

MAY 23, 2023

Hey guys, we will see some of the Best and Unique Machine Learning Projects for final year engineering students in today’s blog. This is going to be a very interesting blog, so without any further due, let’s do it… 1. Self-Organizing Maps In this blog, we will see how we can implement self-organizing maps in Python.

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Mlearning.ai

MAY 21, 2023

Hey guys, we will see some of the Best and Unique Machine Learning Projects with Source Codes in today’s blog. Youtube Comments Extraction and Sentiment Analysis Flask App Hey, guys in this blog we will implement Youtube Comments Extraction and Sentiment Analysis in Python using Flask. This is going to be a very short blog.

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Mlearning.ai

JUNE 6, 2023

In today’s blog, we will see some very interesting Python Machine Learning projects with source code. Doctor-Patient Appointment System in Python using Flask Hey guys, in this blog we will see a Doctor-Patient Appointment System for Hospitals built in Python using Flask. I myself made this as my final year major project.

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Pickl AI

SEPTEMBER 3, 2023

The following blog will provide you a thorough evaluation on how Anomaly Detection Machine Learning works, emphasising on its types and techniques. On the other hand, 48% use ML and AI for gaining insights into the prospects and customers. Further, it will provide a step-by-step guide on anomaly detection Machine Learning python.

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Mlearning.ai

MAY 23, 2023

Hey guys, in this blog we will see some of the most asked Data Science Interview Questions by interviewers in [year]. Read the full blog here —  [link] Data Science Interview Questions for Freshers 1. It is introduced into an ML Model when an ML algorithm is made highly complex. What is Data Science? What will you do?

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