AWS Machine Learning Blog

Category: SageMaker

Amazon SageMaker supports kNN classification and regression

We’re excited to announce that starting today Amazon SageMaker supports a built-in k-Nearest-Neighbor (kNN) algorithm for solving classification and regression problems. kNN is a simple, interpretable, and surprisingly strong model for multi-class classification, ranking, and regression. Introduction to kNN The idea behind kNN is that similar data points should have the same class, at least […]

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Discover Financial Services applies machine learning through a Robocar event powered by Amazon SageMaker

The Discover Financial Services (DFS) team members who attended AWS re:Invent agreed that the Robocar Rally was an extremely impactful experience. By participating in this hackathon, six members of Discover’s core team received hands-on experience using machine learning (ML) and deep learning on AWS. They had a blast and created lasting memories! Discover’s Cloud Center […]

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Secure prediction calls in Amazon SageMaker with AWS PrivateLink

Amazon SageMaker now supports Amazon Virtual Private Cloud (VPC) Endpoints via AWS PrivateLink so you can initiate prediction calls to your machine learning models hosted on Amazon SageMaker inside your VPC, without going over the internet. Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning […]

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Running Principal Component Analysis in Amazon SageMaker

Principal Component Analysis (PCA) is a very popular technique used by data scientists primarily for dimensionality reduction in numerous applications ranging from stock market prediction to medical image classification. Other uses of PCA include de-noising and feature extraction. PCA is also used as an exploratory data analysis tool. To better understand PCA let’s consider an […]

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Build a serverless frontend for an Amazon SageMaker endpoint

Amazon SageMaker provides a powerful platform for building, training, and deploying machine learning models into a production environment on AWS. By combining this powerful platform with the serverless capabilities of Amazon Simple Storage Service (S3), Amazon API Gateway, and AWS Lambda, it’s possible to transform an Amazon SageMaker endpoint into a web application that accepts […]

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Introduction to the Amazon SageMaker Neural Topic Model

Structured and unstructured data are being generated at an unprecedented rate, so you need the right tools to help organize, search, and understand this vast amount of information, it’s challenging to make the data useful. This is especially true for unstructured data, and it’s estimated that over 80% of the data in enterprises is unstructured. Text analytics […]

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Simplify machine learning with XGBoost and Amazon SageMaker

Machine learning is a powerful tool that has recently enabled use cases that were never previously possible–computer vision, self-driving cars, natural language processing, and more. Machine learning is a promising technology, but it can be complex to implement in practice. In this blog post, we explain XGBoost—a machine learning library that is simple, powerful, and […]

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Amazon SageMaker now supports PyTorch and TensorFlow 1.8

Starting today, you can easily train and deploy your PyTorch deep learning models in Amazon SageMaker. This is the fourth deep learning framework that Amazon SageMaker has added support for, in addition to TensorFlow, Apache MXNet, and Chainer.  Just like with those frameworks, now you can write your PyTorch script like you normally would and […]

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Analyze US census data for population segmentation using Amazon SageMaker

In the United States, with the 2018 midterm elections approaching, people are looking for more information about the voting process. This blog post explores how we can apply machine learning (ML) to better integrate science into the task of understanding the electorate. Typically for machine learning applications, clear use cases are derived from labelled data. […]

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