Build custom Amazon SageMaker PyTorch models for real-time handwriting text recognition

Favorite In many industries, including financial services, banking, healthcare, legal, and real estate, automating document handling is an essential part of the business and customer service. In addition, strict compliance regulations make it necessary for businesses to handle sensitive documents, especially customer data, properly. Documents can come in a variety

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Shared by AWS Machine Learning December 17, 2021

Distributed Mask RCNN training with Amazon SageMakerCV

Favorite Computer vision algorithms are at the core of many deep learning applications. Self-driving cars, security systems, healthcare, logistics, and image processing all incorporate various aspects of computer vision. But despite their ubiquity, training computer vision algorithms, like Mask or Cascade RCNN, is hard. These models employ complex architectures, train

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Shared by AWS Machine Learning December 16, 2021

Train and deploy a FairMOT model with Amazon SageMaker

Favorite Multi-object tracking (MOT) in video analysis is increasingly in demand in many industries, such as live sports, manufacturing, surveillance, and traffic monitoring. For example, in live sports, MOT can track soccer players in real time to analyze physical performance such as real-time speed and moving distance. Previously, most methods

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Shared by AWS Machine Learning December 16, 2021

Process Amazon Redshift data and schedule a training pipeline with Amazon SageMaker Processing and Amazon SageMaker Pipelines

Favorite Customers in many different domains tend to work with multiple sources for their data: object-based storage like Amazon Simple Storage Service (Amazon S3), relational databases like Amazon Relational Database Service (Amazon RDS), or data warehouses like Amazon Redshift. Machine learning (ML) practitioners are often driven to work with objects

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Shared by AWS Machine Learning December 16, 2021

Build GAN with PyTorch and Amazon SageMaker

Favorite GAN is a generative ML model that is widely used in advertising, games, entertainment, media, pharmaceuticals, and other industries. You can use it to create fictional characters and scenes, simulate facial aging, change image styles, produce chemical formulas synthetic data, and more. For example, the following images show the

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Shared by AWS Machine Learning December 16, 2021

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