Machine learning has become core to edge usability, particularly for Internet of Things (IoT) devices. Modern edge devices access an immense amount of data to implement automated decision-making and make accurate predictions. This requires powerful computation, trained machine learning models, always-connected hardware and cloud to provide decision-making intelligence.
Recently, aided by the wide adoption of 5G and the consequent need for low latency decisioning, machine learning at the edge has gained traction.
Machine Learning at the Edge
To be useful, machine learning algorithms should provide accurate, low cost, and low latency results. The distributed edge network might be the optimal way to achieve these needs.
Machine learning models are trained in the more powerful cloud and deployed at the edge data centers or on edge devices for inference. This reduces reliance on the cloud, shares the compute burden, and offers real-time response where required.
How Edge Machine Learning Works
Machine learning is based on algorithms using powerful computation to develop complex models using the data. The ML inferences gained after training of the model are further provided with the dataset to edge devices to make predictions.
Computing resources to build and train such models need the assistance of powerful servers and cloud availability. But fetching inference results in real-time with lowest latency is best done by edge devices nearest to the user. The algorithms can be run either in the cloud or at the edge depending on the computing power required.
This reduces response time and makes it possible to leverage machine learning even without network availability. For example, autonomous vehicles need to adapt to the turns, speed bumps, and potholes in real-time, and a globally-distributed edge network ensures continuous connectivity.
Gradient descent-based edge machine learning focuses on distributing data over multiple edge devices, producing copies of the model locally. These copies are then received by the aggregator which is itself an edge device. If updates are made locally, the aggregator updates it on a distributed edge network. This process is repeated until a particular result is achieved.
The primary purpose of inferencing at the edge is to reduce latency and computation cost, including cloud request/response processing over a network, which is relatively costly and adds latency. It results in big data warehouse attributes with machine learning algorithms on distributed data at the edge instantly.
This approach is ideal in terms of privacy and security as personally identifiable data shouldn’t be moved to different devices.
Edge machine learning is one of the fastest-growing fields in terms of research. Modern microcontroller units (MCU) aid in iterating complex ML algorithms at the edge to ensure real-time performance. As machine learning is all about extracting patterns from data, predictions, and decision-making on the outcomes without human interventions, achieving such functionalities in real-time with edge devices adds to its performance.
Learn more about how Macrometa's ready-to-go industry solutions that offer analytics. and machine learning algorithms to power next-generation technologies with low latency anywhere in the world.