Over the years, the way of perceiving and processing data has evolved due to faster computation and evolution in programming today. The demand for AI in applications these days has multiplied; hence, the use of machine learning has come to exist on various platforms. The need to cater to these AI requirements without lag or latency gave rise to Edge AI.
According to Statista, by 2028, mobile consumers will be the most prominent stakeholders in using Edge AI infrastructure. They would cover around 21.8 per cent of the total infrastructure. Moreover, Edge AI has been multiplying, and almost every industry has been looking into ways to harness its powers to leverage the best machine learning techniques efficiently.
In the recent future, around twenty per cent of the manufacturing industry has planned to adopt Edge AI in their existing products. Moreover, an additional 31 per cent has also shown keen interest in following the trend. Although, before diving deep into why and how Edge AI dominates the market trend, let’s look at what AI, Cloud AI and Edge AI mean, along with their tradeoffs and advantages.
What is AI
Artificial intelligence is an advanced technological innovation which has taken the world by storm. It refers to the cloning of the intelligence of humans into machines. AI integrated into machines are programmed to perform and think similarly to human beings and imitate their actions. Some AI application includes speech recognition, expert systems, machine vision and ‘natural language processing.
What is Cloud AI
In cases where machine learning (ML) combines AI into applications through cloud infrastructure can be termed cloud AI. Most of the providers of cloud services offer a range of artificial intelligence services which developers make use of in applications. In cloud AI, transferring the data requires an internet connection via which data is stored, analysed and processed in the cloud centre.
What is Edge AI
Edge AI, unlike cloud AI, comprises several techniques which bring data collection, processing and analysis to the network edge. In simple words, in edge AI, data collection and computing power are situated in the place where data collection occurs. Edge AI helps bring computing resources nearer to the device or user at the network’s “edge”.
Notable Read: “Edge AI: The future of AI is here!”
Cloud AI and Edge AI as codependent models
Cloud AI and edge AI compliments each other to ensure the models used for AI deployment are constantly improving without sacrificing data quantity and quality. Cloud AI compliments edge AI’s instant immediate decision-making by offering a deeper understanding of longitudinal data.
One of the most significant differences between edge AI and cloud AI is processing data. In the case of cloud AI, data is processed and stored within the cloud environment; this helps in offering exceptional flexibility in architecture and design. In the case of cloud AI, devices need internet connectivity to function correctly and make effective decisions; this can lead to potential security and latency issues.
In contrast, edge AI uses the data to process at the network’s extreme edge, which permits making secured real-time decisions at the edge without depending on an internet connection.
Edge AI’s target devices are often witnessed as neither fast enough nor powerful to meet the performance entirely, size, power consumption and memory necessities of edge. Moreover, the selection of ML algorithms and their sizes are based on restricted memory and size capacity.
On the other hand, the cloud’s storage and computing capabilities are better. Cloud AI possess the flexibility to serve a vast range of devices without any limitation on size, memory, power and performance, where the only limit is its cost.
A trade-off between cloud AI vs edge AI
Several developers have used cloud and edge AI; both have their own advantages and disadvantages. However, it is essential to draw a thorough compare and contrast study between the two for the developers to choose the most suitable one for their purpose.
Latency:
The amount of time required from sending a request to receiving the response is referred to as latency. The architecture of cloud AI is quick, but it is not quick enough for apps requiring real-time responses. One such example is self-driving cars which require instant responses for knowing when to accelerate, brake or take a turn.
Connectivity:
Self-driving cars do not have time to suffer downtimes, mainly when it is being used, as that would jeopardise and compromise the passengers’ safety along with others on the road. It needs to be connected all the time as a little lack of connectivity can malfunction a vehicle or completely stop working. Including this, other safety-critical apps need processing in real-time and continuous connectivity that edge AI offers.
Security:
Edge devices offer improved privacy and security as the information is stored on the device itself and requires no sending to the cloud. Specific applications such as identity authentication via fingerprint or facial recognition, which entail delicate information such as medical records and other confidential information, are best to be stored locally for privacy and security concerns.
Processing power:
The processing power of cloud AI is more significant than edge devices. Edge devices prove to be more challenging to replace or upgrade than the ones offered by the cloud.
Digestive Read: “How Edge AI is addressing the key challenges of Cloud Computing”.
Energy consumption:
In edge AI, energy consumption can be a constraint because a constant power supply is needed while working on edge devices. On the other hand, in the case of cloud computing, limited issues regarding energy consumption are faced as compared to the level of edge AI.
With the increased storage capacity and advanced processing power in AI and edge computing, AI industry has seen a rapid rise in demand throughout the market. However, after going through the various tradeoffs and differences, it is easy to lean towards Edge AI and confirm its bright future when competing against traditional cloud AI implementation. The significant difference is how data is processed on either platform.
Despite being contenders, both techniques seldom underplay each other and complement and multiply the system’s efficiency. Due to this complementary prowess, Edge AI has been holding its ground substantially and has become a popular choice, showcasing a bright future.
