Category: Analytics

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 […]

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 […]

In this blog post, I’ll show you how to perform exploratory analysis on massive corporate data sets in HAQM SageMaker. From your Jupyter notebook running on HAQM 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 HAQM SageMaker notebook, in a seamless experience.

September 8, 2021: HAQM Elasticsearch Service has been renamed to HAQM OpenSearch Service. See details. 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 […]

April 2023 Update: Starting January 31, 2024, you will no longer be able to access AWS DeepLens through the AWS management console, manage DeepLens devices, or access any projects you have created. To learn more, refer to these frequently asked questions about AWS DeepLens end of life. Yesterday we announced the integration of AWS DeepLens with […]

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. HAQM released a dataset to the public with over 130 million product reviews in multiple […]

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.

In this blog post, we’ll use your webcam on your laptop to send a live feed to an HAQM Kinesis Video Stream. From there, a processor within HAQM 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 HAQM Simple Notification Service (HAQM SNS).

August 30, 2023: HAQM Kinesis Data Analytics has been renamed to HAQM Managed Service for Apache Flink. Read the announcement in the AWS News Blog and learn more. 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 […]

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.