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Use Cases Edge Intelligence

Edge Intelligence

The concept of edge intelligence (EI) introduces a paradigm shift with regard to acquiring, storing, and processing data: the data processing is placed at the edge between the data source (e.g. a sensor) and the IoT core and storage services located in the cloud. As such, the literal definition of edge and intelligence is: the ability to acquire and apply knowledge and skills is shifted towards the outside of an area, here the core communication network or the cloud.

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Expedia Hosted by 2lemetry Through AWS
Expedia Hosted by 2lemetry Through AWS
 Expedia is committed to continuous innovation, technology, and platform improvements to create a great experience for its customers. The Expedia Worldwide Engineering (EWE) organization supports all websites under the Expedia brand. Expedia began using Amazon Web Services (AWS) in 2010 to launch Expedia Suggest Service (ESS), a typeahead suggestion service that helps customers enter travel, search, and location information correctly. According to the company’s metrics, an error page is the main reason for site abandonment. Expedia wanted global users to find what they were looking for quickly and without errors. At the time, Expedia operated all its services from data centers in Chandler, AZ. The engineering team realized that they had to run ESS in locations physically close to customers to enable a quick and responsive service with minimal network latency.
Employing Intel Deep Learning SDK Toward Bettering Image Recognition Models
Employing Intel Deep Learning SDK Toward Bettering Image Recognition Models
In this case study, the challenge explored involves LeNet*, one of the prominent image recognition topologies for handwritten digit recognition.   In the case study, we dive into how the training tool can be used to visually set up, tune, and train the Mixed National Institute of Standards and Technology (MNIST) dataset on Caffe* optimized for Intel® architecture. Data scientists are the intended audience.
Koito - Reality AI: Next-generation Adaptive Driving Beam (ADB) smart headlight
Koito - Reality AI: Next-generation Adaptive Driving Beam (ADB) smart headlight
The current versions of Adaptive Driving Beams available in Japan and Europe are using traditional machine vision techniques like template matching. Those machine learning techniques perform very well in constrained environments but are prone to many false positive when used in the dynamic, real world, with a lot more variations in targets and backgrounds.

The edge analytics market is estimated to grow from USD 1.94 billion in 2016 to USD 7.96 billion by 2021, at a Compound Annual Growth Rate (CAGR) of 32.6%.

Source: marketsandmarkets

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