AWS Machine Learning Blog

Category: Apache MXNet on AWS

Model Server for Apache MXNet v1.0 released

AWS recently released Model Server for Apache MXNet (MMS) v1.0, featuring a new API for managing the state of the service, which includes the ability to dynamically load models during runtime, to lower latency, and to have higher throughput. In this post, we will explore the new features and showcase the performance gains of the […]

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Using deep learning on AWS to lower property damage losses from natural disasters

Natural disasters like the 2017 Santa Rosa fires and Hurricane Harvey cost hundreds of billions of dollars in property damages every year, wreaking economic havoc in the lives of homeowners. Insurance companies do their best to evaluate affected homes, but it could take weeks before assessments are available and salvaging and protecting the homes can […]

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Pixm takes on phishing attacks with deep learning using Apache MXNet on AWS

Despite numerous cybersecurity efforts, phishing attacks are still on the rise. Phishing is a form of fraud where perpetrators pretend to be reputable companies and attempt to get individuals to reveal personal information, such as passwords and credit card numbers. It’s the most common social tactic.  93 percent of all breaches today start with phishing […]

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Curalate makes social sell with AI using Apache MXNet on AWS

Curalate helps brands convert social influence into sales. The Philadelphia-based startup makes it easy for digital-savvy consumers to make associations between products they see on social channels and where they can buy the very same products. Previously, consumers had to hunt through online catalogs on their own to find a specific product from a social […]

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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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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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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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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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Apache MXNet Model Server adds optimized container images for Model Serving at scale

Today AWS released Apache MXNet Model Server (MMS) v0.3, which streamlines the deployment of model serving for production use cases. The release includes pre-built container images that are optimized for deep learning workloads on GPU and CPU. This enables engineers to set up a scalable serving infrastructure. To learn more about Apache MXNet Model Server […]

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