AWS Machine Learning Blog

Many organizations use Amazon SageMaker for their machine learning (ML) requirements and source data from a data lake stored on Amazon Simple Storage Service (Amazon S3). The petabyte scale source data on Amazon S3 may not always be clean because data lakes ingest data from several source systems, such as like flat files, external feeds, […]

With the 2020 football season kicking off, Amazon Web Services (AWS) is continuing its work with the National Football League (NFL) on several ongoing game-changing initiatives. Specifically, the NFL and AWS are teaming up to develop state-of-the-art cloud technology using machine learning (ML) aimed at aiding the officiating process through real-time football detection. As a […]

Optical character recognition (OCR) technology, which enables extracting text from an image, has been around since the mid-20th century, and continues to be a research topic today. OCR and document understanding are still vibrant areas of research because they’re both valuable and hard problems to solve. AWS has been investing in improving OCR and document […]

In recent years, businesses are increasingly looking for ways to integrate the power of machine learning (ML) into business decision-making. This post demonstrates the use case of creating an optimal incentive program to offer customers identified as being at risk of leaving for a competitor, or churning. It extends a popular ML use case, predicting […]

In this post, we show you how to select the right metadata for your use case when building a recommendation engine using Amazon Personalize. The aim is to help you optimize your models to generate more user-relevant recommendations. We look at which metadata is most relevant to include for different use cases, and where you […]

The modeling phase is a highly iterative process in machine learning (ML) projects, where data scientists experiment with various data preprocessing and feature engineering strategies, intertwined with different model architectures, which are then trained with disparate sets of hyperparameter values. This highly iterative process with many moving parts can, over time, manifest into a tremendous […]

Amazon Lex provides the power of automatic speech recognition (ASR) for converting speech to text, along with natural language understanding (NLU) for recognizing user intents. This combination allows you to develop sophisticated conversational interfaces using both voice and text for chatbots, IVR bots, and voicebots. This week, we’re announcing Amazon Lex support for British English […]

University of Illinois, Urbana Champaign (UIUC) has partnered with the Amazon Machine Learning Solutions Lab to help UIUC football coaches prepare for games more efficiently and improve their odds of winning. Previously, coaches prepared for games by creating a game planning sheet that only featured types of plays for a certain down and distance, and […]

Amazon Comprehend is a natural language processing (NLP) service that uses machine learning (ML) to find insights and relationships like people, places, sentiments, and topics in unstructured text. You can now use Amazon Comprehend ML capabilities to detect and redact personally identifiable information (PII) in customer emails, support tickets, product reviews, social media, and more. […]

The volume of user-generated content (UGC) and third-party content has been increasing substantially in sectors like social media, ecommerce, online advertising, and photo sharing. However, such content needs to be reviewed to ensure that end-users aren’t exposed to inappropriate or offensive material, such as nudity, violence, adult products, or disturbing images. Today, some companies simply […]