Category: Learning Levels

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. You may even use multiple IDEs within a project. Amazon SageMaker lets ML teams choose to work from fully managed, cloud-based environments within […]

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 following figure, this is an effective technique to implement multi-tenancy of models within your machine learning (ML) infrastructure. We have seen software as a […]

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 simultaneously take advantage of graph structure and node/edge properties to solve the task at hand. Traditional methods either only use […]

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 background, we index our worldwide catalog of products, deploy highly scalable AWS fleets, and use advanced machine learning (ML) to match […]

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 apply unique rules associated with its business background while processing these documents. How to extract information accurately and process them flexibly is […]

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 usually means a delay in resolving the IT service request. This can result in reduced productivity, a decrease in customer satisfaction, […]

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 proprietary fraud detection and prevention technology, and AWS have launched GDxAFD, a fraud detection solution tailored for mobile apps that integrates GD’s device intelligence […]

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 preestablished guidelines of acceptable behavior that are specific to the platform and its audience. When a platform moderates content, acceptable user-generated content (UGC) […]

August 30, 2023: Amazon Kinesis Data Analytics has been renamed to Amazon Managed Service for Apache Flink. Read the announcement in the AWS News Blog and learn more. February 9, 2024: Amazon Kinesis Data Firehose has been renamed to Amazon Data Firehose. Read the AWS What’s New post to learn more. Near-real-time delivery of data […]

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 typically tested in the cloud. Creating and running a full pipeline during experimentation adds unwanted overhead and cost to the development lifecycle. In this post, we […]