AWS Machine Learning Blog

We’re excited to announce that Amazon Personalize now lets you measure how your personalized recommendations can help you achieve your business goals. After specifying the metrics that you want to track, you can identify which campaigns and recommenders are most impactful and understand the impact of recommendations on your business metrics. All customers want to […]

This post is co-written by Zdenko Estok, Cloud Architect at Accenture and Sakar Selimcan, DeepRacer SME at Accenture. With the increasing use of artificial intelligence (AI) and machine learning (ML) for a vast majority of industries (ranging from healthcare to insurance, from manufacturing to marketing), the primary focus shifts to efficiency when building and training […]

This post is co-written with Jonathan Jung, Mike Band, Michael Chi, and Thompson Bliss at the National Football League. A coverage scheme refers to the rules and responsibilities of each football defender tasked with stopping an offensive pass. It is at the core of understanding and analyzing any football defensive strategy. Classifying the coverage scheme […]

Amazon Textract is a machine learning (ML) service that automatically extracts text, handwriting, and data from any document or image. AnalyzeDocument Signatures is a feature within Amazon Textract that offers the ability to automatically detect signatures on any document. This can reduce the need for human review, custom code, or ML experience. In this post, […]

Earth’s changing climate poses an increased risk of drought due to global warming. Since 1880, the global temperature has increased 1.01 °C. Since 1993, sea levels have risen 102.5 millimeters. Since 2002, the land ice sheets in Antarctica have been losing mass at a rate of 151.0 billion metric tons per year. In 2022, the […]

Machine learning (ML) has become ubiquitous. Our customers are employing ML in every aspect of their business, including the products and services they build, and for drawing insights about their customers. To build an ML-based application, you have to first build the ML model that serves your business requirement. Building ML models involves preparing the […]

This post is co-written with Stephen Aylward, Matt McCormick, Brianna Major from Kitware and Justin Kirby from the Frederick National Laboratory for Cancer Research (FNLCR). Amazon SageMaker Studio Lab provides no-cost access to a machine learning (ML) development environment to everyone with an email address. Like the fully featured Amazon SageMaker Studio, Studio Lab allows […]

Amazon SageMaker has announced the support of three new completion criteria for Amazon SageMaker automatic model tuning, providing you with an additional set of levers to control the stopping criteria of the tuning job when finding the best hyperparameter configuration for your model. In this post, we discuss these new completion criteria, when to use them, and […]

This blog post is co-written with Bruno Mateus, Jonathan Diedrich and Crispim Tribuna at Talkdesk. Contact centers are using artificial intelligence (AI) and natural language processing (NLP) technologies to build a personalized customer experience and deliver effective self-service support through conversational bots. This is the first of a two-part series dedicated to the integration of […]

Researchers continue to develop new model architectures for common machine learning (ML) tasks. One such task is image classification, where images are accepted as input and the model attempts to classify the image as a whole with object label outputs. With many models available today that perform this image classification task, an ML practitioner may […]