AWS Machine Learning Blog

This post is co-written by Ian Avilez and Tim Pavlick from HawkEye 360. HawkEye 360 is a commercial radio frequency (RF) constellation, data, and analytics provider. Their signals of interest include very high frequency (VHF) push-to-talk radios, maritime radar systems, Automatic Identification System (AIS) beacons, emergency beacons, and more. The signals of interest library will […]

Today, we’re excited to announce a new similar items recommendation recipe (aws-similar-items) in Amazon Personalize that helps you leverage your users’ interaction histories and what you know about the items in your catalog to deliver relevant recommendations. Across Amazon, we provide personalized experiences for each of our users, and based on a user’s interests, we […]

The University of Maryland, Baltimore County’s Bina lab is a multidisciplinary research lab for employing advanced computer vision, machine learning (ML), and remote sensing techniques to discover new knowledge of our environment, especially in the Arctic and Antarctic regions. The lab’s work is supported by NSF BIGDATA awards (IIS-1947584, IIS-1838230), the NSF HDR Institute award […]

This is a guest post by Imperva, a solutions provider for cybersecurity.  Imperva is a cybersecurity leader, headquartered in California, USA, whose mission is to protect data and all paths to it. In the last few years, we’ve been working on integrating machine learning (ML) into our products. This includes detecting malicious activities in databases, […]

When companies deal with data that comes from various sources or the collection of this data has changed over time, the data often becomes difficult to organize. Perhaps you have product category names that are similar but don’t match, and on your website you want to surface these products as a group. Therefore, you need […]

Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models at any scale. Typically, you can use the pre-built and optimized training and inference containers that have been optimized for AWS hardware. Although those containers cover many deep learning workloads, you may have […]

As you plan your new Amazon Lex application, the following conversation design best practices can help your team succeed in creating a great customer experience. In our previous post, we discussed how to create the foundation for good conversation design. We explored the business value of good conversational design and provided some tips on building a team. We also talked about the importance of identifying use cases to create an informed foundation for your conversational interfaces. Throughout our series, we emphasize the importance of keeping the customer at the focus of your design process—this will improve the customer experience.

New trends are shaping the credit card industry as shopping habits have rapidly evolved over the last 18 months. The pandemic has accelerated the move away from cash towards cards. Card issuers are transforming their products to better serve cardmembers through innovations such as contactless payments and mobile wallet. The rapid change in consumer behavior […]

Fraud teams need a secure, fast, and flexible transaction fraud detection solution to combat global fraudsters. Unlike many solutions on the market, Amazon Fraud Detector allows you to tailor your fraud detection efforts specifically to your data and business challenge while also bringing the latest in fraud detection machine learning (ML) technology to bear on […]

The ability to predict that a particular customer is at a high risk of churning, while there is still time to do something about it, represents a huge potential revenue source for every online business. Depending on the industry and business objective, the problem statement can be multi-layered. The following are some business objectives based […]