AWS Machine Learning Blog

Category: Artificial Intelligence

Labeling data for 3D object tracking and sensor fusion in Amazon SageMaker Ground Truth

Amazon SageMaker Ground Truth now supports labeling 3D point cloud data. For more information about the launched feature set, see this AWS News Blog post. In this blog post, we specifically cover how to perform the required data transformations of your 3D point cloud data to create a labeling job in SageMaker Ground Truth for […]

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How REA Group implemented automated image compliance with Amazon Rekognition

Amazon Rekognition is a machine learning (ML) based image and vision analysis service that can identify objects, people, text, scenes, and activities in images and videos, and detect any inappropriate content. Amazon Rekognition text detection enables you to recognize and extract textual content from images and videos. For example, in image sharing and social media […]

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Introducing Recommendation Filters in Amazon Personalize

Today, we are pleased to announce the addition of Recommendation Filters in Amazon Personalize, which improve the relevance of personalized recommendations by filtering out recommendations for products that users have already purchased, videos they have already watched, or other digital content they have already consumed. Receiving such recommendations can be a frustrating experience for users, […]

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Creating a persistent custom R environment for Amazon SageMaker

Amazon SageMaker is a fully managed service that allows you to build, train, and deploy machine learning (ML) models quickly. Amazon SageMaker removes the heavy lifting from each step of the ML process to make it easier to develop high-quality models. In August 2019, Amazon SageMaker announced the availability of the pre-installed R kernel in […]

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Coding with R on Amazon SageMaker notebook instances

Many AWS customers already use the popular open-source statistical computing and graphics software environment R for big data analytics and data science. Amazon SageMaker is a fully managed service that lets you build, train, and deploy machine learning (ML) models quickly. Amazon SageMaker removes the heavy lifting from each step of the ML process to […]

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Using Amazon SageMaker with Amazon Augmented AI for human review of Tabular data and ML predictions

Tabular data is a primary method to store data across multiple industries, including financial, healthcare, manufacturing, and many more. A large number of machine learning (ML) use cases deal with traditional structured or tabular data. For example, a fraud detection use case might be tabular inputs like a customer’s account history or payment details to […]

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Managing missing values in your target and related datasets with automated imputation support in Amazon Forecast

Amazon Forecast is a fully managed service that uses machine learning (ML) to generate highly accurate forecasts without requiring any prior ML experience. Forecast is applicable in a wide variety of use cases, including estimating product demand, supply chain optimization, resource planning, energy demand forecasting, and computing cloud infrastructure usage. With Forecast, there are no […]

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Pioneering personalized user experiences at StockX with Amazon Personalize

This is a guest post by Sam Bean and Nic Roberts II at StockX. In their own words, “StockX is a Detroit startup company revolutionizing ecommerce with a unique Bid/Ask marketplace—our platform models the New York Stock Exchange and treats goods like sneakers and streetwear as high-value, tradable commodities. With a transparent market experience, StockX […]

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Multi-GPU distributed deep learning training at scale with Ubuntu18 DLAMI, EFA on P3dn instances, and Amazon FSx for Lustre

AWS Deep Learning AMI (Ubuntu 18.04) is optimized for deep learning on EC2 Accelerated Computing Instance types, allowing you to scale out to multiple nodes for distributed workloads more efficiently and easily. It has a prebuilt Elastic Fabric Adapter (EFA), Nvidia GPU stack, and many deep learning frameworks (TensorFlow, MXNet, PyTorch, Chainer, Keras) for distributed […]

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Introducing Amazon SageMaker Components for Kubeflow Pipelines

Today we’re announcing Amazon SageMaker Components for Kubeflow Pipelines. This post shows how to build your first Kubeflow pipeline with Amazon SageMaker components using the Kubeflow Pipelines SDK. Kubeflow is a popular open-source machine learning (ML) toolkit for Kubernetes users who want to build custom ML pipelines.  Kubeflow Pipelines is an add-on to Kubeflow that lets […]

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