In a previous post, we defined the concept of edge AI and discussed its benefits and use cases. With edge AI, the processing power needed to enable artificial intelligence is pushed out to the network's edge, closer to the sensors and other devices that collect data. Small, inexpensive chips now have the power to handle AI computations and can be embedded in a wide range of devices.
Edge AI eliminates the latency associated with sending data to the cloud. That doesn’t mean the cloud is irrelevant, however. Like the relationship between cloud computing and edge computing, Edge AI and cloud AI are complementary technologies that work together to provide greater insight into data.
Industrial automation, the Internet of Things (IoT), and edge computing go hand-in-hand. Systems, devices, and applications at the network edge are constantly collecting and generating data. For example, sensors might measure temperature, humidity, and other parameters within coolers that store biomedical samples. If that data is sent to the cloud, there can be delays before the system alerts operators that the parameters have gone out of range. Edge AI allows for real-time data analysis and even “smart” equipment that can respond to problems autonomously.
Edge AI has its tradeoffs, however. Developers must consider edge devices' compute power, memory, and power consumption requirements, which can limit the machine learning algorithm and model size. Generally, small edge data centers are used for inference, while the training of the model takes place in the cloud, powered by massive hyperscale data centers, which has no such limitations.
Another distinction is the scope of the analytics to be performed. Edge AI applications are designed to monitor specific devices, analyze a limited amount of data, and predict the behavior of specific systems. Cloud AI can ingest and analyze vast amounts of data from many sources and provide a holistic view of an entire process or ecosystem. It’s important for organizations to apply these two technologies to the correct applications to maximize their value.
Generally, the choice of when to use edge AI vs. cloud AI comes down to the amount of data to be processed and the overall objective of the use case. However, there are several other factors to consider.
In addition to minimizing latency, edge AI eliminates dependence on an Internet connection. If you have devices with slow or intermittent connectivity, edge AI is the better choice. Either model will work if your devices always have a fast Internet connection.
Edge AI gets mixed reviews for security and privacy. On the one hand, it reduces the number of attack vectors and the risk of data leakage in transit to and from the cloud. It also helps organizations meet regulatory requirements for maintaining data locally. However, attackers can exploit vulnerabilities in edge devices to gain access to the network, steal data, or tamper with AI algorithms. These risks can be reduced by robust network authentication and authorization, data encryption, and monitoring and regular updates of AI algorithms.
AI requires a significant amount of data for training. Additionally, edge devices that constantly collect information can quickly accumulate a substantial amount of data. That data must be stored, and edge systems and devices typically don’t have much storage capacity. As a result, storage is one area that strongly favors cloud AI.
Edge AI requires upfront IT infrastructure and equipment investments to be deployed at the edge site. Cloud AI eliminates those investments but comes with ongoing subscription costs that will increase as transaction volumes and storage requirements grow.
Despite its tradeoffs, edge AI can deliver tremendous value for use cases in which low latency is critical. To maximize the value of edge AI, it’s essential to start with a strong foundation. Enconnex EdgeRack micro data centers for edge computing incorporate power, cooling, cable management, and other features to support IT equipment at the edge. Our latest offering, the EdgeRack 5M, offers 5kW of integrated, bottom-mounted cooling with a built-in condensate evaporating device, remote management and monitoring, security features, and more in a standard cabinet footprint. Let our experts guide you through the design of your edge infrastructure. Get in touch today.