AWS Machine Learning Blog

Category: Analytics

Enable smart text analytics using Amazon Elasticsearch Service and Amazon Comprehend

We’re excited to announce an end-to-end solution that leverages natural language processing to analyze and visualize unstructured text in your Amazon Elasticsearch Service domain with Amazon Comprehend in the AWS Cloud. You can deploy this solution in minutes with an AWS CloudFormation template and visualize your data in a Kibana dashboard. Amazon Elasticsearch Service (Amazon ES) […]

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Exploring data warehouse tables with machine learning and Amazon SageMaker notebooks

Are you a data scientist with data warehouse tables that you’d like to explore in your machine learning (ML) environment? If so, read on. In this post, I show you how to perform exploratory analysis on large datasets stored in your data warehouse and cataloged in your AWS Glue Data Catalog from your Amazon SageMaker […]

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Analyze live video at scale in real time using Amazon Kinesis Video Streams and Amazon SageMaker

We are excited to announce the launch of the Amazon Kinesis Video Streams Inference Template (KIT) for Amazon SageMaker. This capability enables customers to attach Kinesis Video streams to Amazon SageMaker endpoints in minutes. This drives real-time inferences without having to use any other libraries or write custom software to integrate the services. The KIT comprises […]

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Shopper Sentiment: Analyzing in-store customer experience

Retailers have been using in-store video to analyze customer behaviors and demographics for many years.  Separate systems are commonly used for different tasks.  For example, one system would count the number of customers moving through a store, in which part of the store those customers linger and near which products.  Another system will hold the store layout, whilst yet […]

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Run SQL queries from your SageMaker notebooks using Amazon Athena

The volume, velocity and variety of data has been ever increasing since the advent of the internet. The problem many enterprises face is managing this “big data” and trying to make sense out of it to yield the most desirable outcome. Siloes in enterprises, continuous ingestion of data in numerous formats, and the ever-changing technology […]

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Access Amazon S3 data managed by AWS Glue Data Catalog from Amazon SageMaker notebooks

In this blog post, I’ll show you how to perform exploratory analysis on massive corporate data sets in Amazon SageMaker. From your Jupyter notebook running on Amazon SageMaker, you’ll identify and explore several corporate datasets in the corporate data lake that seem interesting to you. You’ll discover that each contains a subset of the information you need. You’ll join them to extract the interesting information, then continue analyzing and visualizing your data in your Amazon SageMaker notebook, in a seamless experience.

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Build a document search bot using Amazon Lex and Amazon Elasticsearch Service

People spend a lot of time searching documents. First you go to your document store and then you search for relevant documents. If you’re looking for a text inside the document, then you need to do another search. In this blog post we’ll describe how you can search for a document using voice or text.  […]

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Video analytics in the cloud and at the edge with AWS DeepLens and Kinesis Video Streams

Yesterday we announced the integration of AWS DeepLens with Amazon Kinesis Video Streams, allowing you to easily and securely stream a video feed from AWS DeepLens to Amazon Kinesis Video Streams for analytics, machine learning and other processing. To help you understand the solution that integrates AWS DeepLens and Kinesis Video Streams, we’ll recap the […]

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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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