Build a computer vision model using Amazon Rekognition Custom Labels and compare the results with a custom trained TensorFlow model

Favorite Building accurate computer vision models to detect objects in images requires deep knowledge of each step in the process—from labeling, processing, and preparing the training and validation data, to making the right model choice and tuning the model’s hyperparameters adequately to achieve the maximum accuracy. Fortunately, these complex steps

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Shared by AWS Machine Learning December 16, 2021

Live transcriptions of F1 races using Amazon Transcribe

Favorite The Formula 1 (F1) live steaming service, F1 TV, has live automated closed captions in three different languages: English, Spanish, and French. For the 2021 season, FORMULA 1 has achieved another technological breakthrough, building a fully automated workflow to create closed captions in three languages and broadcasting to 85

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Shared by AWS Machine Learning December 13, 2021

Hierarchical Forecasting using Amazon SageMaker

Favorite Time series forecasting is a common problem in machine learning (ML) and statistics. Some common day-to-day use cases of time series forecasting involve predicting product sales, item demand, component supply, service tickets, and all as a function of time. More often than not, time series data follows a hierarchical

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Shared by AWS Machine Learning December 13, 2021

Plan the locations of green car charging stations with an Amazon SageMaker built-in algorithm

Favorite While the fuel economy of new gasoline or diesel-powered vehicles improves every year, green vehicles are considered even more environmentally friendly because they’re powered by alternative fuel or electricity. Hybrid electric vehicles (HEVs), battery only electric vehicles (BEVs), fuel cell electric vehicles (FCEVs), hydrogen cars, and solar cars are

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Shared by AWS Machine Learning December 9, 2021

Clinical text mining using the Amazon Comprehend Medical new SNOMED CT API

Favorite Mining medical concepts from written clinical text, such as patient encounters, plays an important role in clinical analytics and decision-making applications, such as population analytics for providers, pre-authorization for payers, and adverse-event detection for pharma companies. Medical concepts contain medical conditions, medications, procedures, and other clinical events. Extracting medical

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Shared by AWS Machine Learning December 9, 2021

AWS Deep Learning AMIs: New framework-specific DLAMIs for production complement the original multi-framework DLAMIs

Favorite Since its launch in November 2017, the AWS Deep Learning Amazon Machine Image (DLAMI) has been the preferred method for running deep learning frameworks on Amazon Elastic Compute Cloud (Amazon EC2). For deep learning practitioners and learners who want to accelerate deep learning in the cloud, the DLAMI comes pre-installed

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Shared by AWS Machine Learning December 9, 2021

AWS computer vision and Amazon Rekognition: AWS recognized as an IDC MarketScape Leader in Asia Pacific (excluding Japan), up to 38% price cut, and major new features

Favorite Computer vision, the automatic recognition and description of documents, images, and videos, has far-reaching applications, from identifying defects in high-speed assembly lines, to intelligently automating document processing workflows, and identifying products and people in social media. AWS computer vision services, including Amazon Lookout for Vision, AWS Panorama, Amazon Rekognition,

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Shared by AWS Machine Learning December 7, 2021

Create and manage Amazon EMR Clusters from SageMaker Studio to run interactive Spark and ML workloads – Part 1

Favorite Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning (ML). It provides a single, web-based visual interface where you can perform all ML development steps required to prepare data, as well as build, train, and deploy models. We recently introduced the ability to visually

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Shared by AWS Machine Learning December 3, 2021

Create and manage Amazon EMR Clusters from SageMaker Studio to run interactive Spark and ML workloads – Part 2

Favorite In Part 1 of this series, we offered step-by-step guidance for creating, connecting, stopping, and debugging Amazon EMR clusters from Amazon SageMaker Studio in a single-account setup. In this post, we dive deep into how you can use the same functionality in certain enterprise-ready, multi-account setups. As described in

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Shared by AWS Machine Learning December 3, 2021