AWS Machine Learning Blog

Category: Artificial Intelligence

Announcing the first winner of the AWS DeepRacer League Summit circuit!

Today, at the AWS Summit in Santa Clara, California, we kicked off the 2019 season of the world’s first global autonomous racing league. The AWS DeepRacer League allows developers of all skill levels to get hands on with machine learning through a series of live racing events at AWS Global Summits around the world. The […]

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Reducing deep learning inference cost with MXNet and Amazon Elastic Inference

Amazon Elastic Inference (Amazon EI) is a service that allows you to attach low-cost GPU-powered acceleration to Amazon EC2 and Amazon SageMaker instances. MXNet has supported Amazon EI since its initial release at AWS re:Invent 2018. In this blog post, we’ll explore the cost and performance benefits of using Amazon EI with MXNet. We’ll walk […]

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Control root access to Amazon SageMaker notebook instances

Amazon SageMaker recently introduced the ability to enable and disable root access for notebook users. Before I give you a preview of how you can implement this new feature using the AWS Management Console and Amazon SageMaker API actions, I’ll explain why controlling root access for users is helpful. Amazon SageMaker provides fully managed notebook […]

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AWS Deep Learning AMIs now come with TensorFlow 1.13, MXNet 1.4, and support Amazon Linux 2

The AWS Deep Learning AMIs now come with MXNet 1.4.0, Chainer 5.3.0, and TensorFlow 1.13.1, which is custom-built directly from source and tuned for high-performance training across Amazon EC2 instances. AWS Deep Learning AMIs are now available on Amazon Linux 2 Developers can now use the AWS Deep Learning AMIs and Deep Learning Base AMI on […]

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De-identify medical images with the help of Amazon Comprehend Medical and Amazon Rekognition

Medical images are a foundational tool in modern medicine that enable clinicians to visualize critical information about a patient to help diagnose and treat them. The digitization of medical images has vastly improved our ability to reliably store, share, view, search, and curate these images to assist our medical professionals. The number of modalities for […]

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Map clinical notes to the OMOP Common Data Model and healthcare ontologies using Amazon Comprehend Medical

Being able to describe the health of patients with observational data is an important aspect of our modern healthcare system. The amount of quantifiable personal health information is vast and constantly growing as new healthcare methods, metrics, and devices are introduced. All of this data allows clinicians and researchers to understand how the health of […]

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Become a certified machine learning developer with the new AWS Certified Machine Learning – Specialty certification

Back in November 2018 we announced on this blog that the same machine learning (ML) courses used to train engineers at Amazon are now available to all developers through AWS. Today, we’re letting you know that there is a way to enhance and validate your ability to build, train, tune, and deploy machine learning models […]

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Bring your own hyperparameter optimization algorithm on Amazon SageMaker

In this blog post, we’ll discuss how to implement custom, state-of-the-art hyperparameter optimization (HPO) algorithms to tune models on Amazon SageMaker. Amazon SageMaker includes a built-in HPO algorithm, but provides the flexibility to use your own HPO algorithm. We’ll provide you with a framework to incorporate an HPO algorithm that you choose. However, before we […]

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Model serving with Amazon Elastic Inference

Amazon Elastic Inference (EI) is a service that allows you to attach low-cost GPU-powered acceleration to Amazon EC2 and Amazon SageMaker instances. EI reduces the cost of running deep learning inference by up to 75%. Model Server for Apache MXNet (MMS) enables deployment of MXNet- and ONNX-based models for inference at scale. In this blog […]

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