How emerging technologies will enhance edge computing deployments
Edge computing’s popularity has soared over the past six years as the system allowed, for example, utilities to monitor and manage usage, based on prevailing local conditions. The following article explains the likely ways emerging technologies will enhance edge computing in 2020.
Over the past year, many industry players increased their focus on edge-based solutions, and organizations are now beginning to understand the value true edge computing can bring to their Internet of Things (IoT) and Industrial Internet of Things (IIoT) projects.
Forrester confirmed that the need for on-demand compute and real-time application insights will continue to drive edge computing demand in 2020.
For example, using edge computing solutions, industrial factories can predict in real-time when unexpected failures will occur or if part of a machine might malfunction prior to either of these events happening. Moreover, by merging edge computing capabilities with the power of artificial intelligence (AI), organizations can move beyond traditional analytics capabilities and significantly improve predictive functionality and overall ROI.
As an increasing number of edge-enabled IoT and IIoT projects move to full-scale deployments over the next year, keep an eye on these industry trends:
Organizations will experience a shift from cloud only to cloud-edge hybrid strategies to enable Edge AI and iterative ML modeling and ongoing improvement of outcomes
Being able to analyze high-fidelity, high-resolution, raw machine data in the cloud is often expensive and does not happen in real-time due to transport and ecosystem considerations. Organizations often depend on down-sampled or time deferred data to avoid significant cost constraints, and as a result, organizations miss critical insights as they’re only looking at incomplete datasets.
Instead, by implementing edge-first solutions, organizations can synthesize data locally, identify machine learning inferences on core raw data sets, and deliver enhanced predictive capabilities (versus cloud-heavy, expensive, retroactive insights). By running ‘edgified’ versions of ML models in real-time, organizations enable faster responses to real-time events and the ability to act, react, pro-act to events of interest at the source. This ensures a harmonious interplay of edge and cloud, leveraging the strengths of each ecosystem.
Indeed, in the next few years, more than 40% of organizations’ cloud deployments will include edge computing to address bandwidth bottlenecks, reduce latency, and process data for mission-critical decision support in real-time. These edge-powered, IIoT projects will extract a realistic view of daily machine operations and work towards a new level of predictability that will dramatically alter the industry landscape as we know it. In short, in 2020, cloud-dominated solutions will adopt a more edge-first, or cloud-edge hybrid, approach to drive significant business value.
Organizations will look beyond edge computing to edge AI solutions to deliver optimal ROI
When organizations build ML models, an assumption is made that the model will be accurate for a certain period of time, as the model has been trained on a particular set of data. If new data patterns emerge or if the model has not been trained on all possible data sets or workflows, the model might not continue to provide accurate results. By employing edge AI, the models can be continuously updated with new, meaningful data and the learning sets updated.
For example, in a factory, a model can be deployed to detect defects on a part inspection assembly line or proactively identify patterns that may lead to defects after a period of time. Often, after a few months, the model’s accuracy may diminish due to new data patterns. This can be misleading, and the opportunity cost can be significant if the software uses traditional analytics exclusively.
Using the power of artificial intelligence (AI) at the edge and self-learning models, in 2020, ML models can move beyond traditional analytics capabilities and significantly improve predictive functionality and overall ROI. With edge AI, software can proactively interface with live data streams and cater to intelligence at or near the source, leading to increased overall productivity, efficiency, and cost-savings.
This article was written by Senthil Kumar from Information Management and was legally licensed through the NewsCred publisher network. Please direct all licensing questions to legal@newscred.com.
