Edge intelligence: Machine learning at the enterprise edge

Description

Enterprises and public sector organizations are drowning in real-time data from equipment, assets, suppliers, employees, customers, and city and utility data sources. Hidden insights have the potential to optimize production, transform efficiency, and streamline flows of goods and services, but finding insights cost effectively remains a challenge. Complex, big-data focused, cloud-hosted ML solutions are expensive, slow, and unsuited to real-time data. It’s important to cost-effectively learn on data at the “edge” as it is produced.

Simon Crosby details an architecture for learning on time series data using edge devices, based on the distributed actor model. This approach flies in the face of the traditional wisdom of cloud-based, big-data solutions to ML problems. You’ll see that there are more than enough resources at “the edge” to cost-effectively analyze, learn from, and predict from streaming data on the fly.

The solution relies on two fundamental innovations:

• A distributed actor fabric: Used in application frameworks from Erlang to Orleans, this approach models each entity in the real world as an actor or digital twin. Simon explains how the approach uses a distributed edge compute fabric that is stateful, efficient, secure, and resilient and runs on commodity edge devices and how its creators enhanced the actor model that allows digital twins to learn—on their own real-world data—to predict future performance.
• A self-training, unsupervised ML approach: Crucially, ML must be cost effective and use standard edge hardware, even nontraditional systems, such as ARM CPUs. The edge fabric transforms large volumes of low-value data into low volumes of high-value insights, and each actor predicts its own future behavior affordably, saving bandwidth and avoiding unnecessary storage and cloud processing.

Edge learning delivers new insights fast, specific to the local context, enabling the infrastructure to adapt to changing conditions. Learning at the edge on “high def” data—with many parameters per entity—enables us to avoid overfitting and to gain greater fidelity. The efficient solution of an edge learning model also is maximally efficient in terms of communication, making the edge environment into a parallel machine learning network, distributed across edge nodes.