AWS Machine Learning Blog

Category: SageMaker

Using R with Amazon SageMaker

This blog post describes how to train, deploy, and retrieve predictions from a machine learning (ML) model using Amazon SageMaker and R. The model predicts abalone age as measured by the number of rings in the shell. The reticulate package will be used as an R interface to Amazon SageMaker Python SDK to make API calls to Amazon […]

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Using Pipe input mode for Amazon SageMaker algorithms

Today, we are introducing Pipe input mode support for the Amazon SageMaker built-in algorithms. With Pipe input mode, your dataset is streamed directly to your training instances instead of being downloaded first. This means that your training jobs start sooner, finish quicker, and need less disk space. Amazon SageMaker algorithms have been engineered to be […]

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Perform a large-scale principal component analysis faster using Amazon SageMaker

In this blog post, we conduct a performance comparison for PCA using Amazon SageMaker, Spark ML, and Scikit-Learn on high-dimensional datasets. SageMaker consistently showed faster computational performance. Refer Figures (1) and (2) at the bottom to see the speed improvements. Principal Component Analysis Principal Component Analysis (PCA) is an unsupervised learning algorithm that attempts to […]

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Running fast.ai notebooks with Amazon SageMaker

fast.ai is an organization dedicated to making the power of deep learning accessible to all. They have developed a popular open source deep learning framework called fast.ai. This technology is based on the deep learning library PyTorch, which is focused on usability and allows users to create state-of-the-art models with just a few lines of code in domains […]

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Simulate quantum systems on Amazon SageMaker

Amazon SageMaker is a fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. But besides streamlining the machine learning (ML) workflow, Amazon SageMaker also provides a serverless, powerful, and easy-to-use compute environment to execute and parallelize a large spectrum of scientific computing […]

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Amazon Pinpoint campaigns driven by machine learning on Amazon SageMaker

In this blog post, I want to continue the theme of demonstrating agility, cost efficiency, and how AWS can help you innovate through your customer analytics practice. Many of you are exploring how AI can enhance their customer 360o initiatives. I’ll demonstrate how targeted campaigns can be driven by machine learning (ML) through solutions that leverage Amazon SageMaker and Amazon Pinpoint.

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Use the built-in Amazon SageMaker Random Cut Forest algorithm for anomaly detection

Today, we are launching support for Random Cut Forest (RCF) as the latest built-in algorithm for Amazon SageMaker. RCF is an unsupervised learning algorithm for detecting anomalous data points or outliers within a dataset. This blog post introduces the anomaly detection problem, describes the Amazon SageMaker RCF algorithm, and demonstrates the use of the Amazon […]

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Text Classification with Gluon on Amazon SageMaker and AWS Batch

Our customer had a problem: The manual classification of warranty claims was causing a bottleneck. These claims were based on a text field that explained the event in short detail. An example of that text looked something like this: “The plutonium-fueled nuclear reactor overheated on a hot day in Arizona’s recent inclement weather. Burn damage […]

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Train faster, more flexible models with Amazon SageMaker Linear Learner

Today Amazon SageMaker is launching several additional features to the built-in linear learner algorithm. Amazon SageMaker algorithms are designed to scale effortlessly to massive datasets and take advantage of the latest hardware optimizations for unparalleled speed. The Amazon SageMaker linear learner algorithm encompasses both linear regression and binary classification algorithms. These algorithms are used extensively in […]

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