Artificial intelligence (AI) has always been a prominent technology in modern software development. AI in software solutions has helped businesses analyze mass volumes of data, predict outcomes, find patterns, understand languages, solve customer support challenges, and much more.
But what opportunities does edge computing provide when it comes to software development? How is edge any different from cloud computing, and most importantly, can AI & Edge transform software development?
Let’s find out…
Edge computing is a new type of distributed architecture that uses networked devices such as smartphones, tablets, and other mobile devices to provide cloud-based services to businesses and people at home or on the go.
It allows users’ devices to interact with multiple cloud services rather than having data stored only in local memory or on the device itself. This helps users access important information even when they are not connected to the Internet.
In other words, you do not have to access the cloud whenever you need data. As the name indicates, computing and processing can be done on the edge of a cloud network. Therefore, you may access the data even without entering the cloud or enterprise network.
There is no doubt that artificial intelligence-based software solutions require more computing power, network performance, security, and much more than normal software solutions. Edge AI adds new processing layers between the cloud and user devices, making AI applications more efficient than ever before. Moreover, the edge can divide application estimates between these processing layers, hence increasing the app performance.
Any software solution containing Artificial Intelligence (AI) must run without experiencing a major decrease in speed and accuracy for it to work efficiently. Usually, such smart applications require latency under ten milliseconds for this. However, the response time of modern cloud computing solutions is around 70 milliseconds or more, and wireless connections are significantly slower.
The time it takes to transfer data from the edge to the cloud, or vice versa, is very low compared to traditional approaches like Web API calls or calling backend systems directly using SOAP/REST APIs.
It means the time taken for an AI software solution or mobile application to access data from the cloud directly is much longer.
The existing method of driving data streams via a handful of sizable data centers limits the potential of developing digital technologies. Edge AI, in turn, offers a whole new approach. Instead of implementing algorithms in distant clouds and remote data centres, it does it locally on chips and specialized hardware.
A device may function without maintaining a constant connection to a specific network or the Internet, and it can access external connections and transmit data as needed.
This is because, in edge computing, the data does not need to be processed first before it reaches its final destination on the end-user device. It also means that there are no delays between when you change something in your mobile app and when your backend system receives it. This can dramatically improve how quickly you react to changes in your business model or technical requirements, which will help you avoid problems.
Therefore, when compared to the cloud, an edge computing solution provides the following benefits:
There are also several other advantages of edge computing in Artificial Intelligence solutions; some of the main ones include the following:
The edge can be scaled as needed, which makes it cheaper and more efficient.
The data from the edge is safer and more secure than data from the cloud, so it will be less prone to attacks.
The edge allows you to work faster by connecting with people and other machines in real time, which increases efficiency and productivity.
The data from the edge is encrypted before it reaches the cloud, protecting your information from hackers.
Faster processing means faster response times and higher throughputs on a person or machine level, which means better overall performance for end users or customers!
Answer: Edge Computing removes recurrent data processing from the cloud using resources at the network edge, much nearer to the data source. Therefore, it is used in several modern solutions, including:
Answer: Edge computing is efficient, secure, private, and cost-effective. This makes it easier for businesses to add Internet of Things devices at scale without running the risk of data breaches or network overloads. Moreover, edge offers businesses a layer of resilience and redundancy for mission-critical tasks.
Answer: Edge Artificial Intelligence or Edge AI is a large number of machine learning and AI algorithms that run on a physical hardware device. Edge AI software enables users to get data in real-time because it does not need other systems or internet connections to connect to others.
If your business has an Artificial Intelligence (AI) solution, you need to ensure that the solution works efficiently. At ThinkPalm, we can help you create innovative AI solutions with edge computing capabilities. We also provide AI chatbot development services for businesses across multiple industries. Our testing services ensure that your AI applications or chatbot solutions will work efficiently under different conditions. Are you ready to enhance your AI solution with edge computing or do you want to create a new AI application for your business? Then schedule a FREE consultation with ThinkPalm consultants today.