AWS Machine Learning Blog

Category: Amazon Machine Learning

Run your local machine learning code as Amazon SageMaker Training jobs with minimal code changes

We recently introduced a new capability in the Amazon SageMaker Python SDK that lets data scientists run their machine learning (ML) code authored in their preferred integrated developer environment (IDE) and notebooks along with the associated runtime dependencies as Amazon SageMaker training jobs with minimal code changes to the experimentation done locally. Data scientists typically […]

How Sportradar used the Deep Java Library to build production-scale ML platforms for increased performance and efficiency

This is a guest post co-written with Fred Wu from Sportradar. Sportradar is the world’s leading sports technology company, at the intersection between sports, media, and betting. More than 1,700 sports federations, media outlets, betting operators, and consumer platforms across 120 countries rely on Sportradar knowhow and technology to boost their business. Sportradar uses data […]

Announcing the updated Microsoft OneDrive connector (V2) for Amazon Kendra

Amazon Kendra is an intelligent search service powered by machine learning (ML), enabling organizations to provide relevant information to customers and employees, when they need it. Amazon Kendra uses ML algorithms to enable users to use natural language queries to search for information scattered across multiple data souces in an enterprise, including commonly used document […]

Building with Generative AI on AWS

Announcing New Tools for Building with Generative AI on AWS

The seeds of a machine learning (ML) paradigm shift have existed for decades, but with the ready availability of scalable compute capacity, a massive proliferation of data, and the rapid advancement of ML technologies, customers across industries are transforming their businesses. Just recently, generative AI applications like ChatGPT have captured widespread attention and imagination. We […]

Modulate makes voice chat safer while reducing infrastructure costs by a factor of 5 with Amazon EC2 G5g instances

This is a guest post by Carter Huffman, CTO and Co-founder at Modulate. Modulate is a Boston-based startup on a mission to build richer, safer, more inclusive online gaming experiences for everyone. We’re a team of world-class audio experts, gamers, allies, and futurists who are eager to build a better online world and make voice […]

Create your RStudio on Amazon SageMaker licensed or trial environment in three easy steps

RStudio on Amazon SageMaker is the first fully managed cloud-based Posit Workbench (formerly known as RStudio Workbench). RStudio on Amazon SageMaker removes the need for you to manage the underlying Posit Workbench infrastructure, so your teams can concentrate on producing value for your business. You can quickly launch the familiar RStudio integrated development environment (IDE) […]

Bundesliga Match Fact Ball Recovery Time: Quantifying teams’ success in pressing opponents on AWS

In football, ball possession is a strong predictor for team success. It’s hard to control the game without having control over the ball. In the past three Bundesliga seasons, as well as in the current season (at the time of this writing), Bayern Munich is ranked first in the table and in ball possession percentage, […]

Bundesliga Match Fact Keeper Efficiency: Comparing keepers’ performances objectively using machine learning on AWS

The Bundesliga is renowned for its exceptional goalkeepers, making it potentially the most prominent among Europe’s top five leagues in this regard. Apart from the widely recognized Manuel Neuer, the Bundesliga has produced remarkable goalkeepers who have excelled in other leagues, including the likes of Marc-André ter Stegen, who is a superstar at Barcelona. In […]

Enable fully homomorphic encryption with Amazon SageMaker endpoints for secure, real-time inferencing

This is joint post co-written by Leidos and AWS. Leidos is a FORTUNE 500 science and technology solutions leader working to address some of the world’s toughest challenges in the defense, intelligence, homeland security, civil, and healthcare markets. Leidos has partnered with AWS to develop an approach to privacy-preserving, confidential machine learning (ML) modeling where […]

Use a data-centric approach to minimize the amount of data required to train Amazon SageMaker models

As machine learning (ML) models have improved, data scientists, ML engineers and researchers have shifted more of their attention to defining and bettering data quality. This has led to the emergence of a data-centric approach to ML and various techniques to improve model performance by focusing on data requirements. Applying these techniques allows ML practitioners […]