AWS Machine Learning Blog
Category: Amazon ML Solutions Lab
AWS Deep Learning AMIs: New framework-specific DLAMIs for production complement the original multi-framework DLAMIs
Since its launch in November 2017, the AWS Deep Learning Amazon Machine Image (DLAMI) has been the preferred method for running deep learning frameworks on Amazon Elastic Compute Cloud (Amazon EC2). For deep learning practitioners and learners who want to accelerate deep learning in the cloud, the DLAMI comes pre-installed with AWS-optimized deep learning (DL) frameworks […]
Clinical text mining using the Amazon Comprehend Medical new SNOMED CT API
Mining medical concepts from written clinical text, such as patient encounters, plays an important role in clinical analytics and decision-making applications, such as population analytics for providers, pre-authorization for payers, and adverse-event detection for pharma companies. Medical concepts contain medical conditions, medications, procedures, and other clinical events. Extracting medical concepts is a complicated process due […]
Next Gen Stats Decision Guide: Predicting fourth-down conversion
It is fourth-and-one on the Texans’ 36-yard line with 3:21 remaining on the clock in a tie game. Should the Colts’ head coach Frank Reich send out kicker Rodrigo Blankenship to attempt a 54-yard field goal or rely on his offense to convert a first down? Frank chose to go for it, leading to a […]
Bushfire mitigation through Machine Learning with AusNet and AWS
Eastern Australia is among the most fire-prone regions in the world. Although bushfires are a regular occurrence in Australia, the 2019–2020 bushfire crisis set ablaze over 17 million hectares of land (larger than the size of England), costing the Australian economy more than $100 billion between property, infrastructure, social, and environmental costs. With increasingly extreme […]
Use AutoGluon-Tabular in AWS Marketplace
AutoGluon-Tabular is an open-source AutoML framework that requires only a single line of Python to train highly accurate machine learning (ML) models on an unprocessed tabular dataset. In this post, we walk you through a way of using AutoGluon-Tabular as a code-free AWS Marketplace product. We use this process to train and deploy a highly […]
Process and add additional file formats to your Amazon Kendra Index
If you have a corpus of internal documents that you frequently search through, Amazon Kendra can help you find your content faster and easier. These documents can be in different locations and repositories, and can be structured or unstructured. Amazon Kendra is a fully managed service backed by machine learning (ML). You don’t need to […]
Automated claims processing at Xactware with machine learning on AWS
This blog post was co-authored, and includes an introduction, by Aaron Brunko, Senior Vice President, Claims Product at Xactware. Property insurance claims involving the valuation and replacement of personal belongings can be a painful process for everyone involved after a loss. From catastrophic events such as hurricanes, tornados, and wildfires, to theft and vandalism, claim […]
HawkEye 360 predicts vessel risk using the Deep Graph Library and Amazon Neptune
This post is co-written by Ian Avilez and Tim Pavlick from HawkEye 360. HawkEye 360 is a commercial radio frequency (RF) constellation, data, and analytics provider. Their signals of interest include very high frequency (VHF) push-to-talk radios, maritime radar systems, Automatic Identification System (AIS) beacons, emergency beacons, and more. The signals of interest library will […]
Detect defects in automotive parts with Amazon Lookout for Vision and Amazon SageMaker
According to a recent study, defective products cost industries over $2 billion from 2012–2017. Defect detection within manufacturing is an important business use case, especially in high-value product industries like the automotive industry. This allows for early diagnosis of anomalies to improve production line efficacy and product quality, and saves capital costs. Although advanced anomaly […]
Bring your own container to project model accuracy drift with Amazon SageMaker Model Monitor
The world we live in is constantly changing, and so is the data that is collected to build models. One of the problems that is often seen in production environments is that the deployed model doesn’t behave the same way as it did during the training phase. This concept is generally called data drift or […]