AWS Machine Learning Blog

This post is a guest joint collaboration by Supratim Banerjee of More Retail Limited and Shivaprasad KT and Gaurav H Kankaria of Ganit Inc. More Retail Ltd. (MRL) is one of India’s top four grocery retailers, with a revenue in the order of several billion dollars. It has a store network of 22 hypermarkets and […]

This blog is co-authored by Sarah Jane Hong CSO, Darryl Barnhart CTO, and Ian Thompson CEO of Latent Space and Prem Ranga of AWS. Latent space is a hidden representation of abstract ideas that machine learning (ML) models learn. For example, “dog,” “flower,” or “door” are concepts or locations in latent space. At Latent Space, […]

Amazon Textract is a fully managed machine learning (ML) service that automatically extracts printed text, handwriting, and other data from scanned documents that goes beyond simple optical character recognition (OCR) to identify, understand, and extract data from forms and tables. Amazon Textract can detect text in a variety of documents, including financial reports, medical records, […]

Amazon Rekognition is a computer vision service that makes it easy to add image and video analysis to your applications using proven, highly scalable, deep learning technology that requires no machine learning (ML) expertise to use. With Amazon Rekognition, you can identify objects, people, text, scenes, and activities in images and videos, as well as […]

Video is a highly effective a highly effective way to educate, entertain, and engage users. Your company might carry a large collection of videos that include captions or subtitles. To make these videos accessible to a larger audience, you can provide translated captions and subtitles in multiple languages. In this post, we show you how […]

This is the second in a two part series on Amazon Comprehend custom classification models. In Part 1 of this series, we looked at how to build an AWS Step Functions workflow to automatically build, test, and deploy Amazon Comprehend custom classification models and endpoints. In Part 2, we look at real-time classification APIs, feedback […]

This is the first in a two part series on Amazon Comprehend custom classification models. In Part 1 of this series, we look at how to build an AWS Step Functions workflow to automatically build, test, and deploy Amazon Comprehend custom classification models and endpoints. In Part 2, we will look at real-time classification APIs, […]

Amazon Forecast uses machine learning (ML) to generate more accurate demand forecasts, without requiring any prior ML experience. Forecast brings the same technology used at Amazon.com to developers as a fully managed service, removing the need to manage resources or rebuild your systems. To start generating forecasts through Forecast, you can follow three steps of […]

Have you ever thought about how artificial intelligence could be used to detect events during live sports broadcasts? With machine learning (ML) techniques, we introduce a scalable multimodal solution for event detection on sports video data. Recent developments in deep learning show that event detection algorithms are performing well on sports data [1]; however, they’re […]

In 2019, AWS unveiled Amazon SageMaker Debugger, a SageMaker capability that enables you to automatically detect a variety of issues that may arise while a model is being trained. SageMaker Debugger captures model state data at specified intervals during a training job. With this data, SageMaker Debugger can detect training issues or anomalies by leveraging […]