AWS Machine Learning Blog

Category: Storage

Demystifying machine learning at the edge through real use cases

Edge is a term that refers to a location, far from the cloud or a big data center, where you have a computer device (edge device) capable of running (edge) applications. Edge computing is the act of running workloads on these edge devices. Machine learning at the edge (ML@Edge) is a concept that brings the […]

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Build and deploy a scalable machine learning system on Kubernetes with Kubeflow on AWS

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 […]

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Securely search unstructured data on Windows file systems with the Amazon Kendra connector for Amazon FSx for Windows File Server

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 […]

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Machine learning inference at scale using AWS serverless

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. […]

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Scan Amazon S3 buckets for content moderation using S3 Batch and Amazon Rekognition

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 […]

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Deploy multiple machine learning models for inference on AWS Lambda and Amazon EFS

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 […]

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Train fraudulent payment detection with Amazon SageMaker

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 […]

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Announcing the Amazon S3 plugin for PyTorch

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 […]

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Schedule an Amazon SageMaker Data Wrangler flow to process new data periodically using AWS Lambda functions

Data scientists can spend up to 80% of their time preparing data for machine learning (ML) projects. This preparation process is largely undifferentiated and tedious work, and can involve multiple programming APIs and custom libraries. Announced at AWS re:Invent 2020, Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare data for […]

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How Intel Olympic Technology Group built a smart coaching SaaS application by deploying pose estimation models – Part 1

The Intel Olympic Technology Group (OTG), a division within Intel focused on bringing cutting-edge technology to Olympic athletes, collaborated with AWS Machine Learning Professional Services (MLPS) to build a smart coaching software as a service (SaaS) application using computer vision (CV)-based pose estimation models. Pose estimation is a class of machine learning (ML) model that […]

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