Category: Storage

Pharmaceutical companies seeking approval from regulatory agencies such as the US Food & Drug Administration (FDA) or Japanese Pharmaceuticals and Medical Devices Agency (PMDA) to sell their drugs on the market must submit evidence to prove that their drug is safe and effective for its intended use. A team of physicians, statisticians, chemists, pharmacologists, and […]

Medical imaging techniques like computed tomography (CT), magnetic resonance imaging (MRI), medical x-ray imaging, ultrasound imaging, and others are commonly used by doctors for various reasons. Some examples include detecting changes in the appearance of organs, tissues, and vessels, and detecting abnormalities such as tumors and various other type of pathologies. Before doctors can use […]

October 2023: Starting in April 26th, 2024, you can no longer access Amazon SageMaker Edge Manager. For more information about continuing to deploy your models to edge devices, see SageMaker Edge Manager end of life. Edge is a term that refers to a location, far from the cloud or a big data center, where you […]

In this post, we demonstrate Kubeflow on AWS (an AWS-specific distribution of Kubeflow) and the value it adds over open-source Kubeflow through the integration of highly optimized, cloud-native, enterprise-ready AWS services. Kubeflow is the open-source machine learning (ML) platform dedicated to making deployments of ML workflows on Kubernetes simple, portable and scalable. Kubeflow provides many […]

Critical information can be scattered across multiple data sources in your organization, including sources such as Windows file systems stored on Amazon FSx for Windows File Server. You can now use the Amazon Kendra connector for FSx for Windows File Server to index documents (HTML, PDF, MS Word, MS PowerPoint, and plain text) stored in […]

With the growing adoption of Machine Learning (ML) across industries, there is an increasing demand for faster and easier ways to run ML inference at scale. ML use cases, such as manufacturing defect detection, demand forecasting, fraud surveillance, and many others, involve tens or thousands of datasets, including images, videos, files, documents, and other artifacts. […]

Dealing with content in large scale is often challenging, costly, and a heavy lift operation. The volume of user-generated and third-party content has been increasing substantially in industries like social media, ecommerce, online advertising, and media sharing. Customers may want to review this content to ensure that it follows corporate governance and regulations. But they […]

You can deploy machine learning (ML) models for real-time inference with large libraries or pre-trained models. Common use cases include sentiment analysis, image classification, and search applications. These ML jobs typically vary in duration and require instant scaling to meet peak demand. You want to process latency-sensitive inference requests and pay only for what you […]

The ability to detect fraudulent card payments is becoming increasingly important as the world moves towards a cashless society. For decades, banks have relied on building complex mathematical models to predict whether a given card payment transaction is likely to be fraudulent or not. These models must be both accurate and precise—they must catch fraudulent […]

Amazon S3 plugin for PyTorch is an open-source library which is built to be used with the deep learning framework PyTorch for streaming data from Amazon Simple Storage Service (Amazon S3). With this feature available in PyTorch Deep Learning Containers, you can take advantage of using data from S3 buckets directly with PyTorch dataset and […]