AWS Machine Learning Blog

Leveraging Low Precision and Quantization for Deep Learning Using the Amazon EC2 C5 Instance and BigDL  

by Jason Dai and Joseph Spisak | on | Permalink | Comments |  Share

Recently AWS released the new compute-intensive Amazon EC2 C5 instances, based on the latest generation Intel Xeon Scalable Platinum processors. These instances are designed for compute-heavy applications, and offer a large performance improvement over the C4 instances. They also have additional memory per vCPU, and twice the performance for vector and floating-point workloads. In this […]

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Congratulations to the Winners of the re:Invent Robocar Rally 2017!

To drive awareness of deep learning, machine learning, and the internet of things in autonomous driving, AWS hosted a hackathon—the Robocar Rally—at re:Invent in November 2017. We kicked off Robocar Rally in September with a series of blog posts and Twitch streams.  At re:Invent, we had 100 attendees come for a hands-on two-day hackathon using deep […]

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Updated AWS Deep Learning AMIs: New Versions of TensorFlow, Apache MXNet, Keras, and PyTorch

We’re excited to update the AWS Deep Learning AMIs with significantly faster training on NVIDIA Tesla V100 “Volta” GPUs across many frameworks, including TensorFlow, PyTorch, Keras, and the latest Apache MXNet 1.0 release. There are two main flavors of the AMIs available today. The Conda-based AWS Deep Learning AMI packages the latest point releases of […]

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Introducing Model Server for Apache MXNet

Earlier this week, AWS announced the availability of Model Server for Apache MXNet, an open source component built on top of Apache MXNet for serving deep learning models. Apache MXNet is a fast and scalable training and inference framework with an easy-to-use, concise API for machine learning. With Model Server for Apache MXNet, engineers are […]

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Serverless Unsupervised Machine Learning with AWS Glue and Amazon Athena

Have you ever had the need to segment a data set based on some of its attributes? K-means is one of the most common machine learning algorithms used to segment data. The algorithm works by separating data into different groups, called clusters. Each sample is assigned a cluster so that the samples assigned to the […]

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Announcing the Availability of ONNX 1.0

Today, Amazon Web Services (AWS), Facebook and Microsoft are pleased to announce that the Open Neural Network Exchange (ONNX) format is production ready. ONNX is an open standard format for deep learning models that enables interoperability between deep learning frameworks such as Apache MXNet, Caffe2, Microsoft Cognitive Toolkit, and PyTorch. ONNX 1.0 enables users to […]

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Assisting People at Haptik Using Amazon Polly

by Swapan Rajdev and Ranvijay Jamwal | on | in Amazon Polly* | Permalink | Comments |  Share

Haptik is India’s first personal-assistant app. Users can use the app to plan travel, check in for flights, book taxis, and set reminders. And of all the different features, the most important and frequently used is the Reminders feature. People use Haptik to set wake-up calls, set up reminders to drink water, call people at different times, send greetings to others for different occasions, and much more. Through the reminders feature, users will receive notifications on the app along with a phone call at a requested time, relating the reminder message. In this post, we will cover how we use machine learning and text-to-speech (TTS) to set reminders for users – to call them at the given time to remind them of their tasks. We will cover how Amazon Polly helped us make personalized calls to our users and helped us scale our reminders feature to millions of users.

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AWS Contributes to Milestone 1.0 Release of Apache MXNet Including the Addition of a New Model Serving Capability

Today AWS announced contributions to the milestone 1.0 release of the Apache MXNet deep learning engine and the introduction of a new model serving capability for MXNet. These new capabilities (1) simplify training and deploying deep learning models, (2) enable implementation of cutting-edge performance enhancements, and (3) provide easy interoperability between deep learning frameworks. In […]

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Whooshkaa and Amazon Polly: Combining Eyes and Ears to Widen Publishing Horizons

Based in Australia, Whooshkaa is a creative audio-on-demand podcast platform that helps publishers and advertisers reach their audiences. We’re always trying new products and techniques, and combining them to pioneer new solutions for our customers. The Amazon Polly Text-To-Speech (TTS) feature is a great example of this. Already, we have top-tier publishers, sporting bodies, and Australia’s biggest telecommunications company using Amazon Polly to augment their established delivery methods.

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AWS and Caltech Partner to Accelerate AI and Machine Learning Through a New Research Collaboration

by Joseph Spisak and Adam Wierman | on | Permalink | Comments |  Share

From autonomous robotics to state of-the-art computer vision, Caltech and Amazon have a lot in common, including the belief that pushing the boundaries of artificial intelligence (AI) and machine learning (ML) will not only disrupt industries, but it will fundamentally change the nature of scientific research. We believe these technologies have the potential to transform […]

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