AWS Machine Learning Blog

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 […]

In April 2020, AWS and Facebook announced the launch of TorchServe to allow researches and machine learning (ML) developers from the PyTorch community to bring their models to production more quickly and without needing to write custom code. TorchServe is an open-source project that answers the industry question of how to go from a notebook […]

Live video streams are continuously generated across industries including media and entertainment, retail, and many more. Live events like sports, music, news, and other special events are broadcast for viewers on TV and other online streaming platforms. AWS customers increasingly rely on machine learning (ML) to generate actionable insights in real time and deliver an […]

In an effort to drive customer service improvements, many companies record the phone conversations between their customers and call center representatives. These call recordings are typically stored as audio files and processed to uncover insights such as customer sentiment, product or service issues, and agent effectiveness. To provide an accurate analysis of these audio files, […]