Category: Learning Levels

As machine learning (ML) goes mainstream and gains wider adoption, ML-powered applications are becoming increasingly common to solve a range of complex business problems. The solution to these complex business problems often requires using multiple ML models. These models can be sequentially combined to perform various tasks, such as preprocessing, data transformation, model selection, inference […]

Today, Amazon SageMaker Canvas introduces the ability to use the Quick build feature with time series forecasting use cases. This allows you to train models and generate the associated explainability scores in under 20 minutes, at which point you can generate predictions on new, unseen data. Quick build training enables faster experimentation to understand how […]

Exploratory data analysis (EDA) is a common task performed by business analysts to discover patterns, understand relationships, validate assumptions, and identify anomalies in their data. In machine learning (ML), it’s important to first understand the data and its relationships before getting into model building. Traditional ML development cycles can sometimes take months and require advanced […]

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 […]