AWS Machine Learning Blog

Amazon Personalize can now unlock intrinsic signals in your catalog to recommend similar items

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 […]

How NSF’s iHARP researchers are enabling active learning for polar ice analysis using Amazon SageMaker and Amazon A2I

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 […]

How Imperva expedites ML development and collaboration via Amazon SageMaker notebooks

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, […]

Organize product data to your taxonomy with Amazon SageMaker

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 […]

Train and deploy deep learning models using JAX with Amazon SageMaker

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 […]

How to approach conversation design: Getting started with Amazon Lex (Part 2)

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.

Build conversational experiences for credit card services using Amazon Lex

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 […]

Detect online transaction fraud with new Amazon Fraud Detector features

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 […]


Build, tune, and deploy an end-to-end churn prediction model using Amazon SageMaker Pipelines

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 […]

Build your own brand detection and visibility using Amazon SageMaker Ground Truth and Amazon Rekognition Custom Labels – Part 2: Training and analysis workflows

In Part 1 of this series, we showed how to build a brand detection solution using Amazon SageMaker Ground Truth and Amazon Rekognition Custom Labels. The solution was built on a serverless architecture with a custom user interface to identify a company brand or logo from video content and get an in-depth view of screen […]