AWS Machine Learning Blog

Category: Analytics

How to scale sentiment analysis using Amazon Comprehend, AWS Glue and Amazon Athena

Today consumers are encouraged to express their satisfaction or frustration with a company or product through social media, blogs, and review platforms. Sentiment analysis can help companies better understand their customers’ opinions and needs and make more informed business decisions. Amazon released a dataset to the public with over 130 million product reviews in multiple […]

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Maximize training performance with Gluon data loader workers

With recent advances in CPU and GPU technology, training complex and deep neural network models in a few hours is within reach for many state of-the-art deep models. However, when you use a system with such high processing throughput potential, the required data for the processing pipeline must be ready before each iteration.

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Easily perform facial analysis on live feeds by creating a serverless video analytics environment using Amazon Rekognition Video and Amazon Kinesis Video Streams

In this blog post, we’ll use your webcam on your laptop to send a live feed to an Amazon Kinesis Video Stream. From there, a processor within Amazon Rekognition Video analyzes the feed and compares it to a collection we create. The output matches will get sent to us via an email through an integration with AWS Lambda and Amazon Simple Notification Service (Amazon SNS).

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Build automatic analysis of body language to gauge attention and engagement using Amazon Kinesis Video Streams and Amazon AI Services

This is a guest blog post by Ned T. Sahin, PhD (Brain Power LLC and Harvard University), Runpeng Liu (Brain Power LLC and MIT), Joseph Salisbury, PhD (Brain Power LLC), and Lillian Bu (Brain Power LLC and MIT).  Producers of content (from ads to games to teaching materials) usually judge the success of their content […]

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Use facial recognition to deliver high-end consumer experience with Amazon Kinesis Video Streams and Amazon Rekognition Video

Whatever your use case, real-time face recognition with Kinesis Video Streams and Rekognition Video is easy to set up and doesn’t require expensive hardware. The entire system built here is serverless and Rekognition Video qualifies for the AWS Free Tier.

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Detect sentiment from customer reviews using Amazon Comprehend

In today’s world, public content has never been more relevant. Data from customer reviews is being used as a tool to gain insight into consumption-related decisions as the understanding of its associated sentiment grants businesses invaluable market awareness and the ability to proactively address issues early. Sentiment analysis uses a process to computationally determine whether […]

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Build a social media dashboard using machine learning and BI services

In this blog post we’ll show you how you can use Amazon Translate, Amazon Comprehend, Amazon Kinesis, Amazon Athena, and Amazon QuickSight to build a natural-language-processing (NLP)-powered social media dashboard for tweets. Social media interactions between organizations and customers deepen brand awareness. These conversations are a low-cost way to acquire leads, improve website traffic, develop […]

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Build Amazon SageMaker notebooks backed by Spark in Amazon EMR

Introduced at AWS re:Invent in 2017, Amazon SageMaker provides a fully managed service for data science and machine learning workflows. One of the important parts of Amazon SageMaker is the powerful Jupyter notebook interface, which can be used to build models. You can enhance the Amazon SageMaker capabilities by connecting the notebook instance to an […]

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Serverless Unsupervised Machine Learning with AWS Glue and Amazon Athena

Have you ever had the need to segment a data set based on some of its attributes? K-means is one of the most common machine learning algorithms used to segment data. The algorithm works by separating data into different groups, called clusters. Each sample is assigned a cluster so that the samples assigned to the […]

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Distributed Inference Using Apache MXNet and Apache Spark on Amazon EMR

In this blog post we demonstrate how to run distributed offline inference on large datasets using Apache MXNet (incubating) and Apache Spark on Amazon EMR. We explain how offline inference is useful, why it is challenging, and how you can leverage MXNet and Spark on Amazon EMR to overcome these challenges. Distributed inference on large […]

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