Can Edge AI reduce the costs of data processing and management

First, let's define Edge AI. The term combines the concepts of edge computing and Artificial Intelligence (AI). Edge computing is where data processing is done near where the data was created, rather than transferring it to a faraway centralised cloud for processing. For instance, cloud-based security cameras must send the video data to a cloud-based server for processing; only then can it be used for analysis.

In contrast, edge computing is when the data is processed near the "edge" of the network where data is collected (like an IoT device). It eliminates the need to send large volumes of data to a cloud or other offsite storage and processing location, saving time and money, and of course, this also hits on the sustainability and green IT agenda. When you add AI capabilities, the same device can make decisions and act locally on the data.

In the case of the security camera, that could mean facial recognition, object detection, and other analytics are done directly at the camera, reducing the amount of data sent to a cloud-based server.

There are several benefits of Edge AI when it comes to data processing and management.

Lower latency and improved performance

The number of IoT devices, sensors, and other data sources connected to the internet is growing rapidly. When you factor in the need to transfer all of that data to a cloud-based server for processing, it can lead to a bottleneck in bandwidth and latency issues.

Edge computing significantly reduces these problems since the data does not have to travel long distances. Edge AI can process the data locally, reducing latency and improving performance.

Reduced bandwidth requirements

Organisations can save money on expensive bandwidth costs by using Edge AI. Since the data does not have to be transferred over a network, there is less strain on the network and lower bandwidth requirements. Edge AI can also significantly reduce storage costs since only necessary data needs to be stored in cloud-based servers or other offsite locations.

Better data privacy

Cybercrime is a very real threat. As more data is stored in the cloud, the risk of a breach increases. Edge AI can help reduce these risks by locally processing data and eliminating the need to send the data to offsite, shared locations. In addition, Edge AI can help reduce costs associated with compliance since it removes the need to transfer sensitive data over a network. That could mean fewer audits and lower overhead costs for organisations.

Offline functionality

Organisations depending on a reliable internet connection for data processing need to consider the possibility of an outage. Edge AI eliminates this risk by running algorithms and decision-making processes locally, even without an internet connection. This could be especially useful in environments with unreliable or slow connections.

Edge AI is a cost-effective option for organisations looking to reduce the costs associated with data processing and management. By bringing processing capabilities closer to where data is created, Edge AI can reduce costs associated with:

Sensors

The world of IoT depends on sensors to collect data, but they can be expensive. Edge AI helps organisations save money by reducing the number of sensors needed since more complex tasks can be performed locally without additional sensors or hardware. Cameras, microphones, and other AI-enabled devices can be used as multi-purpose devices, thus reducing costs.

Computing systems

Edge AI can also help reduce costs associated with computing systems. By utilizing local processing power (like a laptop or tablet), organisations can save money on expensive cloud-based computing systems. This could lead to significant cost savings in the long run. Computing systems are sized and scaled according to the data they'll process. Edge AI can reduce the data these systems need to process, reducing costs.

Network

When used in an organisation’s IoT ecosystem, Edge AI can run on a hardwired or Wi-Fi network, eliminating the need for a cellular or satellite connection. Since they probably already have a local network, it renders the cost of a separate network unnecessary. On the other hand, cellular or satellite connections will require an additional data plan. Using local networks, organisations transferring video or other high-bandwidth data can reap huge savings.

Applications

When looking to implement an AI solution, organisations often develop custom applications. The cost of an AI software development team can be quite high. In most cases, pre-built applications are available that can be used with Edge AI. Organisations do not have to pay for custom solutions and still get the same functionality. These apps are customizable and can be tailored to an organization's specific needs.

Regarding managing data, Edge AI can be a valuable tool for organisations looking to reduce costs. Management software like AWS IoT, Azure IoT, or NVIDIA Fleet Command makes setting up and managing costs associated with Edge AI easier. They will scale up and down according to the usage, avoiding unnecessary costs associated with over-provisioning.

Additionally, real-time analytics can significantly reduce the time and money needed to identify environmental or production line problems. It can also help provide predictive analytics, giving organisations insight into future trends.

Considering the new infrastructure

Hesitation to implement Edge AI stems from the need for new infrastructure. As mentioned in the introduction, organisations have just started investing in cloud computing and might still be finding their footing. Edge AI requires organisations to invest in new infrastructure – one that could be expensive, depending on the scope of its implementation.

While the upfront costs might be high, the long-term benefits of Edge AI can help organisations recoup their initial investments. By reducing computational costs and increasing efficiency, organizations can save time and money.