Remove insights cloud-and-it-lifecycle resource robotic-process-automation
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Demystifying machine learning at the edge through real use cases

AWS Machine Learning

Edge is a term that refers to a location, far from the cloud or a big data center, where you have a computer device (edge device) capable of running (edge) applications. ML@Edge is important for many scenarios where raw data is collected from sources far from the cloud. Poor or non-existing connectivity to the cloud.

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MLOps at the edge with Amazon SageMaker Edge Manager and AWS IoT Greengrass

AWS Machine Learning

By deploying a variety of edge IoT devices such as cameras, thermostats, and sensors, you can collect data, send it to the cloud, and build machine learning (ML) models to predict anomalies, failures, and more. In this post, you learn how to answer all these questions and build an end-to-end solution for automating your ML@Edge pipeline.

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Women of Influence: The Top 25 Innovative CX Leaders

Netomi

From introducing cryptocurrency to the Latin America market, to offering a free learning online platform for students and playing a key role in the digital transformation of health services, these leaders are making waves in the CX space, and inspiring others along the way. One of her key drivers is employee motivation and empowerment.