AWS Machine Learning Blog

Some quick thoughts on the public discussion regarding facial recognition and Amazon Rekognition this past week

We have seen a lot of discussion this past week about the role of Amazon Rekognition in facial recognition, surveillance, and civil liberties, and we wanted to share some thoughts.

Amazon Rekognition is a service we announced in 2016. It makes use of new technologies – such as deep learning – and puts them in the hands of developers in an easy-to-use, low-cost way. Since then, we have seen customers use the image and video analysis capabilities of Amazon Rekognition in ways that materially benefit both society (e.g. preventing human trafficking, inhibiting child exploitation, reuniting missing children with their families, and building educational apps for children), and organizations (enhancing security through multi-factor authentication, finding images more easily, or preventing package theft). Amazon Web Services (AWS) is not the only provider of services like these, and we remain excited about how image and video analysis can be a driver for good in the world, including in the public sector and law enforcement.

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The importance of hyperparameter tuning for scaling deep learning training to multiple GPUs

Parallel processing with multiple GPUs is an important step in scaling training of deep models. In each training iteration, typically a small subset of the dataset, called a mini-batch, is processed. When a single GPU is available, processing of the mini-batch in each training iteration is handled by this GPU. When training with multiple GPUs, […]

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Building your personal translator with Amazon Translate and Amazon Polly

The most common challenge we can face when traveling abroad is the language barrier. Whether lost or not, we’ll have to say at least one of these: “Where is the best place to eat and drink?”, “Where is this hotel?”, and “Where is the bathroom?” Now imagine a more difficult scenario: We’re traveling to Spain […]

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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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Amazon Translate is now supported in AWS Mobile SDK for Android and iOS

Amazon Translate is a neural machine translation service that delivers fast, high-quality, and affordable language translation. Support for Amazon Translate API is now available in the AWS Mobile SDK for Android and iOS. Now, you can use the AWS Mobile SDK to develop and publish multilingual mobile apps quickly and easily with Amazon Translate. By […]

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Model Server for Apache MXNet adds support for serving Gluon models

Today AWS released Model Server for Apache MXNet (MMS) v0.4, which adds support for serving Gluon models. Gluon is an imperative and dynamic interface for MXNet, which enables rapid model development, while maintaining MXNet performance. With this release, MMS adds support for packaging and serving Gluon models at scale. In this blog post, we will […]

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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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Apache MXNet (incubating) adds support for Keras 2

The Keras-MXNet deep learning backend is available now, thanks to contributors to the Keras and Apache MXNet (incubating) open source projects. Keras is a high-level neural network API written in Python. It’s popular for its fast and easy prototyping of CNNs and RNNs. Keras developers can now use the high-performance MXNet deep learning engine for […]

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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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