AWS Machine Learning Blog

Reducing deep learning inference cost with MXNet and Amazon Elastic Inference

Note: Amazon Elastic Inference is no longer available. Please see Amazon SageMaker for similar capabilities. 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, […]

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 […]

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 […]

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 […]

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 […]

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 […]

Bring your own hyperparameter optimization algorithm on Amazon SageMaker

July 2023: This post is outdated. We recommend referring to Amazon SageMaker Automatic Model Tuning now supports three new completion criteria for hyperparameter optimization for the latest solution. 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 […]

Model serving with Amazon Elastic Inference

Note: Amazon Elastic Inference is no longer available. Please see Amazon SageMaker for similar capabilities. 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 […]

Easily perform bulk label quality assurance using Amazon SageMaker Ground Truth

In this blog post we’re going to walk you through an example situation where you’ve just built a machine learning system that labels your data at volume and you want to perform manual quality assurance (QA) on some of the labels. How can you do so without overwhelming your limited resources?  We’ll show you how, […]

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