AWS Machine Learning Blog

Industries like healthcare, media, and social media platforms use image analysis workflows to identify objects and entities within pictures to understand the whole image. For example, an ecommerce website might use objects present in an image to surface relevant search results. Sometimes image analysis may be difficult when images are blurry or more nuanced. In […]

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 […]

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 […]

This blog post was last reviewed or updated April, 2022 with updates to how filters are configured. 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 […]

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 […]

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 […]

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 […]

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 […]

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 […]

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 […]