AWS Machine Learning

Founded more than 91 years ago in Seattle, John L. Scott Real Estate’s core value is Living Life as a Contribution®. The firm helps homebuyers find and buy the home of their dreams, while also helping sellers mo […]

Machine learning (ML) teams need the flexibility to choose their integrated development environment (IDE) when working on a project. It allows you to have a productive developer experience and innovate at speed. […]

Amazon SageMaker multi-model endpoint (MME) enables you to cost-effectively deploy and host multiple models in a single endpoint and then horizontally scale the endpoint to achieve scale. As illustrated in the […]

If you have searched for an item to buy on amazon.com, you have used Amazon Search services. At Amazon Search, we’re responsible for the search and discovery experience for our customers worldwide. In the b […]

Amazon SageMaker Pipelines is a continuous integration and continuous delivery (CI/CD) service designed for machine learning (ML) use cases. You can use it to create, automate, and manage end-to-end ML workflows. […]

Amazon Neptune ML is a machine learning (ML) capability of Amazon Neptune that helps you make accurate and fast predictions on your graph data. Under the hood, Neptune ML uses Graph Neural Networks (GNNs) to […]

This post was co-written with Tobias Wenzel, Software Engineering Manager for the Intuit Machine Learning Platform.
We all appreciate the importance of a high-quality and reliable machine learning (ML) model when […]

A massive amount of business documents are processed daily across industries. Many of these documents are paper-based, scanned into your system as images, or in an unstructured format like PDF. Each company may […]

Enterprises often deal with large volumes of IT service requests. Traditionally, the burden is put on the requester to choose the correct category for every issue. A manual error or misclassification of a ticket […]

In this post, we present a solution that combines rich mobile device intelligence with customized machine learning (ML) modeling to help you catch fraudsters who exploit mobile apps.
GrabDefence (GD), Grab’s p […]

In this post, we present a solution that combines rich mobile device intelligence with customized machine learning (ML) modeling to help you catch fraudsters who exploit mobile apps.
GrabDefence (GD), Grab’s p […]

Content moderation is the process of screening and monitoring user-generated content online. To provide a safe environment for both users and brands, platforms must moderate content to ensure that it falls within […]

Digital is the new normal, and there’s no going back. Every year, consumers visit, on average, 191 websites or services requiring a user name and password, and the digital footprint is expected to grow e […]

This post was co-written with Robert Berger and Adine Deford from InformedIQ.
InformedIQ is the leader in AI-based software used by the nation’s largest financial institutions to automate loan processing v […]

Model training forms the core of any machine learning (ML) project, and having a trained ML model is essential to adding intelligence to a modern application. A performant model is the output of a rigorous and […]

Synamedia is a leading video technology provider addressing the needs for premium video service providers and direct-to-consumer (D2C) with a comprehensive solution portfolio. Synamedia solutions spread across […]

Near-real-time delivery of data and insights enable businesses to rapidly respond to their customers’ needs. Real-time data can come from a variety of sources, including social media, IoT devices, infrastructure m […]

Face-off Probability is the National Hockey League’s (NHL) first advanced statistic using machine learning (ML) and artificial intelligence. It uses real-time Player and Puck Tracking (PPT) data to show viewers w […]

Machine learning (ML) has improved business across industries in recent years—from the recommendation system on your Prime Video account, to document summarization and efficient search with Alexa’s voice ass […]

Creating robust and reusable machine learning (ML) pipelines can be a complex and time-consuming process. Developers usually test their processing and training scripts locally, but the pipelines themselves are […]