As data volumes, both data that exists and the rate at which new data is generated, continue to grow, and as our desire to make more agile and quicker data-driven decisions strengthens, the potential of Edge Computing becomes more and more relevant.

The world, specifically the “things”, around us emit potentially infinite data points which we are likely just scraping the surface of possibilities on how to utilize. Whether gauges in an office space measuring temperature and movement in the room or equipment in a manufacturing facility or the ball and athletes in our favorite sporting event – everything offers data with the obvious challenge being the acquisition, interpretation, storage, analysis, and initiation of action of that data. The general idea of Edge Computing, with a subset capability being Edge AI, is that the latency, costs, and feasibility of transferring data from Edge Hardware (such as IoT devices) to a central area for processing and storage limits the speed and effectiveness of using that data. So, why can’t we collect, store, analyze, and apply algorithms to that data using the Edge Hardware itself outside of one’s main network?

In this recorded webcast Thorogood Data and Analytics consultant Ben Dunmire illustrates that the prospect of Edge Computing offers an interesting proposition given reduced latency, higher speeds and agility, reduced dependence on bandwidth, and scalability.

What is covered?

• Based on research and latest experiences with our customers, where Edge Computing (and Edge AI) could play a role and how capabilities in the space may develop.
• The current toolsets that can be leveraged for Edge Computing across the major cloud vendors, and potential synergies with existing common architectures.
• Why this topic matters and why you should monitor its evolution.

Is it for you?

• Are you currently working on or exploring use cases where Edge Computing could be utilized, such as collecting and leveraging IoT data?
• Have you heard of Edge Computing and interested in its potential applicability in data and AI architectures?
• Are you interested to learn more about a new topic and its relevance to the data and AI space?