AWS Machine Learning Blog

Optimizing I/O for GPU performance tuning of deep learning training in Amazon SageMaker

GPUs can significantly speed up deep learning training, and have the potential to reduce training time from weeks to just hours. However, to fully benefit from the use of GPUs, you should consider the following aspects: Optimizing code to make sure that underlying hardware is fully utilized Using the latest high performant libraries and GPU […]

Giving your content a voice with the Newscaster speaking style from Amazon Polly

Audio content consumption has grown exponentially in the past few years. Statista reports that podcast ad revenue will exceed a billion dollars in 2021. For the publishing industry and content providers, providing audio as an alternative option to reading could improve engagement with users and be an incremental revenue stream. Given the shift in customer […]

Accelerating innovation: How serverless machine learning on AWS powers F1 Insights

FORMULA 1 (F1) turns 70 years old in 2020 and is one of the few sports that combines real-time skill with engineering and technical prowess. Technology has always played a central role in F1; where the evolution of the rules and tools is built into the DNA of F1. This keeps fans engaged and drivers […]

Building a custom Angular application for labeling jobs with Amazon SageMaker Ground Truth

As a data scientist attempting to solve a problem using supervised learning, you usually need a high-quality labeled dataset before starting your model building. Amazon SageMaker Ground Truth makes dataset building for a different range of tasks, like text classification and object detection, easier and more accessible to everyone. Ground Truth also helps you build […]

2019 Q4 recipients of AWS Machine Learning Research Awards

The AWS Machine Learning Research Awards (MLRA) aims to advance machine learning (ML) by funding innovative research and open-source projects, training students, and providing researchers with access to the latest technology. Since 2017, MLRA has supported over 180 research projects from 73 schools and research institutes in 13 countries, with topics such as ML algorithms, […]

Cisco uses Amazon SageMaker and Kubeflow to create a hybrid machine learning workflow

This is a guest post from members of Cisco’s AI/ML best practices team, including Technical Product Manager Elvira Dzhuraeva, Distinguished Engineer Debo Dutta, and Principal Engineer Amit Saha. Cisco is a large enterprise company that applies machine learning (ML) and artificial intelligence (AI) across many of its business units. The Cisco AI team in the […]

Deriving conversational insights from invoices with Amazon Textract, Amazon Comprehend, and Amazon Lex

Organizations across industries have a large number of physical documents such as invoices that they need to process. It is difficult to extract information from a scanned document when it contains tables, forms, paragraphs, and check boxes. Organization have been addressing these problems with manual effort or custom code or by using Optical Character Recognition […]

How Euler Hermes detects typo squatting with Amazon SageMaker

This is a guest post from Euler Hermes. In their own words, “For over 100 years, Euler Hermes, the world leader in credit insurance, has accompanied its clients to provide simpler and safer digital products, thus becoming a key catalyzer in the world’s commerce.” Euler Hermes manages more than 600,000 B2B transactions per month and […]

Building a visual search application with Amazon SageMaker and Amazon ES

September 8, 2021: Amazon Elasticsearch Service has been renamed to Amazon OpenSearch Service. See details. Sometimes it’s hard to find the right words to describe what you’re looking for. As the adage goes, “A picture is worth a thousand words.” Often, it’s easier to show a physical example or image than to try to describe […]

Introducing the open-source Amazon SageMaker XGBoost algorithm container

XGBoost is a popular and efficient machine learning (ML) algorithm for regression and classification tasks on tabular datasets. It implements a technique known as gradient boosting on trees and performs remarkably well in ML competitions. Since its launch, Amazon SageMaker has supported XGBoost as a built-in managed algorithm. For more information, see Simplify machine learning […]