Edge computing helps make data storage and computation more accessible to users. This is achieved by running operations on local devices like laptops, Internet of Things (IoT) devices, or dedicated edge servers. Edge processes are not affected by the latency and bandwidth issues that often hamper the performance of cloud-based operations.
Edge AI combines edge computing with artificial intelligence (AI). This involves running AI algorithms on local devices with edge computing capacity. Edge AI does not require connectivity and integration between systems, allowing users to process data on the device in real time.
The majority of AI processes are currently performed in cloud-based centers, because they require substantial computing capacity. The downside is that connectivity or network issues can result in downtime or significant slowdown of the service. Edge AI eliminates these issues by making AI processes an integral part of edge computing devices. This enables users to save time by aggregating data and serving users, without communicating with other physical locations.
This is part of our series of articles about machine learning operations.
In this article, you will learn:
The most important advantage of Edge AI is that it brings high-performance computing capabilities to the edge, where sensors and IoT devices are located. AI edge computing enables AI applications to run directly on field devices, processing field data and run machine learning (ML) and deep learning (DL) algorithms.
Data processing in the cloud takes seconds. Data processing at the edge, on the other hand, can take milliseconds or less. For example, autonomous vehicles performing data processing at the edge can make decisions much faster than if data is processed in the cloud. Since these decisions impact human lives, near real-time data processing is critical.
Edge AI operations perform the majority of data processing locally on an edge device. This means that less data is sent to the cloud and other external locations. As a result, the risk that data might be misappropriated or mishandled is reduced.
However, this does not mean that data is secured or protected from hackers and other security threats. For these purposes, the Trusted Platform Group has created the TPM 2.0 hardware security standard, which ensures that edge devices have secure data storage, encrypted authentication, and data integrity auditing.
Edge AI does most of its data processing locally, sending less data over the internet and thus saving a lot of Internet bandwidth. Also the cost of cloud-based AI services can be high. Edge AI lets you use expensive cloud resources as a post-processing data store that collects data for future analysis, not for real-time field operations.
Because Edge AI processes data at the local level, it saves energy costs. Edge computing devices are designed with highly efficient power consumption, means that the power requirements for running AI at the edge are much lower than in cloud data centers.
There is a wide range of Edge AI applications. Notable examples include facial recognition and real-time traffic updates on semi-autonomous vehicles, connected devices, and smartphones. Additionally, video games, robots, smart speakers, drones, wearable health monitoring devices, and security cameras are all starting to support Edge AI.
Here are a few areas where Edge AI will grow in usage and importance:
Edge AI technology can be applied to any purpose. During the COVID-19 crisis, for example, AI-powered solutions were deployed to deliver accurate information in real time. For example, in healthcare, AI deployed in field medical devices helps monitor, test and treat patients more effectively.
Let’s look at three different approaches to deploying AI training and inference, and their pros and cons.
Cloud Computing AI
Cloud computing provided highly scalable, low cost hardware, which was compelling for AI because it allowed organizations to train large-scale models quickly. However, while the cloud is highly suitable for model training, it may be challenging for inference for inference—using AI models to provide predictions in response to user queries.
Using the cloud for inference raises several challenges:
With Edge AI, AI models operate on the edge device, with no latency and no need for an Internet connection. This makes it possible to perform much faster inference and support real-time use cases.
However, Edge AI also raises several issues, because models need to be trained on an ongoing basis using data from the edge devices:
A new pattern known as federated learning can resolve the issues of cloud computing and edge-based AI. This pattern works as follows:
Kubernetes, the platform on which the Run:AI scheduler is based, has a lightweight version called K3s, designed for resource-constrained computing environments like Edge AI. Run:AI automates and optimizes resource management and workload orchestration for machine learning infrastructure. With Run:AI, you can run more workloads on your resource-constrained servers.
Here are some of the capabilities you gain when using Run:AI:
Run:AI simplifies machine learning infrastructure pipelines, helping data scientists accelerate their productivity and the quality of their deep learning models.
Learn more about the Run:AI GPU virtualization platform.