IIoT Progress Report
AI and hybrid edge-cloud to dominate IoT in 2022 SOURCE: IO TECH
Top predictions for edge computing are related to technology advancements, architectures and impact to industries. The effect of edge computing on blockchain, and other digital ledger technologies, will become clearer in a few years. Similarly, 5G’s impact on edge computing is still too early to predict.
One prediction for 2022 is that organizations are finding that processing edge data needs to be performed at the edge, and in the cloud or enterprise. FIVE PREDICTIONS FOR EDGE COMPUTING in 2022 include a proliferation of artificial intelligence (AI) and machine learning at the IOT edge and edge-cloud architectures as the norm. IOTech’s has reported a series of 2022 predictions are related to technology advancements, architectures and impact to industries. The company believes that the effect of edge computing on blockchain and other digital ledger technologies will become clearer in a few years. Similarly, 5G’s impact on edge computing is still too early to predict. “This past year, we’ve seen edge computing emerge from pilot programs to deployments,” said Jim White, CTO, IOTech. “We believe 2022 will be the year that edge computing is fully integrated into the architecture of every major industrial IoT system.” 02.202 2
Prediction 1: There will be pervasive adoption of AI/ML at the edge
Prediction 2: Hybrid edge-cloud architectures will be the norm
The new status quo is that edge systems will incorporate AI and machine learning. Simple rules engines and edge analytics are already at the edge. Today, organizations demand more intelligence at the edge. The raw compute to run AI/ML at the edge was a prohibiting factor, but this is no longer the case. While training ML systems will largely occur in the cloud or in the enterprise, ML models running on lighter AI runtime engines at the edge are more common place and will soon be the norm. Visual inference has been a leading use case, but other AI/ML solutions are soon to follow. Edge platform providers will play a key role in developing solutions that can easily integrate AI/ML technologies.
It’s not edge compute “or” cloud compute, it’s a case of “and”. Organizations are finding that processing edge data needs to be performed at the edge and in the cloud or enterprise. Although initially there was much excitement related to the cloud providers reaching down to the edge, the reality is that there are significant challenges in moving all edge data to the cloud and performing all the processing in the cloud. The cost of data transport, latency issues and security/data privacy concerns are among the chief challenges. Likewise, the raw processing power of the edge and ability to do deeper exploration of the edge data over longer periods of time for better insights means edge computing alone is not a solution.
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