AWS Machine Learning Blog

Optimized TensorFlow 1.8 now available in the AWS Deep Learning AMIs to accelerate training on Amazon EC2 C5 and P3 instances

The AWS Deep Learning AMIs for Ubuntu and Amazon Linux now come with advanced optimizations for TensorFlow 1.8 to deliver higher-performance training for Amazon EC2 C5 and P3 instances. For CPU-based training scenarios, the Amazon Machine Images (AMIs) now include TensorFlow 1.8, built with Intel’s Advanced Vector Instructions (AVX), SSE, and FMA instruction sets to accelerate vector and floating-point computations. The […]

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Running fast.ai notebooks with Amazon SageMaker

Update 25 JAN 2019: fast.ai has released a new version of their library and MOOC making the following blog post outdated. For the latest instructions on setting up the library and course on a SageMaker Notebook instance please refer to the instructions outlined here: https://course.fast.ai/start_sagemaker.html fast.ai is an organization dedicated to making the power of deep learning accessible […]

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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 pre-trained models with Apache MXNet

In this blog post, I’ll show you how to use multiple pre-trained models with Apache MXNet. Why would you want to try multiple models? Why not just pick the one with the best accuracy? As we will see later in the blog post, even though these models have been trained on the same data set and optimized for maximum accuracy, they do behave slightly differently on specific images.

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Maximize training performance with Gluon data loader workers

With recent advances in CPU and GPU technology, training complex and deep neural network models in a few hours is within reach for many state of-the-art deep models. However, when you use a system with such high processing throughput potential, the required data for the processing pipeline must be ready before each iteration.

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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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