AWS Machine Learning Blog

Category: Analytics

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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Run Deep Learning Frameworks with GPU Instance Types on Amazon EMR

Today, AWS is excited to announce support for Apache MXNet and new generation GPU instance types on Amazon EMR, which enables you to run distributed deep neural networks alongside your machine learning workflows and big data processing. Additionally, you can install and run custom deep learning libraries on your EMR clusters with GPU hardware. Through […]

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Capture and Analyze Customer Demographic Data Using Amazon Rekognition & Amazon Athena

Millions of customers shop in brick and mortar stores every day. Currently, most of these retailers have no efficient way to identify these shoppers and understand their purchasing behavior. They rely on third-party market research firms to provide customer demographic and purchase preference information.

This blog post walks you how you can use AWS services to identify purchasing behavior of your customers. We show you:

How retailers can use captured images in real time.
How Amazon Rekognition can be used to retrieve face attributes like age range, emotions, gender, etc.
How you can use Amazon Athena and Amazon QuickSight to analyze the face attributes.
How you can create unique insights and learn about customer emotions and demographics.
How to implement serverless architecture using AWS managed services.

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Build PMML-based Applications and Generate Predictions in AWS

If you generate machine learning (ML) models, you know that the key challenge is exporting and importing them into other frameworks to separate model generation and prediction. Many applications use PMML (Predictive Model Markup Language) to move ML models from one framework to another. PMML is an XML representation of a data mining model. In […]

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