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Edge AI vs. Cloud AI: Which is Best for Your Business? – EnterpriseNetworkingPlanet

In an ideal deployment, all workloads would be centralized in the cloud to enjoy the benefits of scale and simplicity. However, concerns about latency, security, bandwidth, and autonomy call for an artificial intelligence (AI) model deployment at the edge.

These deployments can take on the form of edge AI and/or cloud AI, each offering their own potential unique use cases, benefits, and challenges. With this in mind, it will take careful consideration when choosing the best model for your business.

Edge AI and Cloud AI as Complementary Models

Edge AI and cloud AI play a complementary role in ensuring the models serving AI deployments are continuously improving without compromising on data quality and quantity. Cloud AI complements the instant decision-making of edge AI by providing deeper insights for more longitudinal data.

The greatest difference between cloud AI and edge AI is where data is processed. Cloud AI processes and stores data in a cloud environment, which offers greater flexibility in design and architecture. However, devices would require Internet connectivity to correctly function and make decisions, leading to potential latency and security issues.

By comparison, edge AI processes data at the extreme edge. This allows secure real-time decision-making at the edge, independent of a connection.

The target devices for edge AI are often neither powerful nor fast enough to fully meet the memory, performance, size, and power consumption requirements of the edge. Furthermore, the selection of machine learning algorithms and their sizes is based on limited size and memory capacity.

On the other hand, the compute and storage capabilities of the cloud mean that cloud AI can flexibly serve a wide variety of devices without restrictions on memory, size, performance, and power, with the trade-off being the cost.

Infrastructure and Interoperability

Enterprises can combine edge AI and cloud AI to eliminate disconnected data silos that inhibit the sharing of intelligence to provide value to different areas of the enterprise. Using edge AI alone for AI and Internet of Things (IoT) applications may hinder data from being shared across wider IT infrastructures, which introduces the risk of failing to fully derive valuable insights from data.

To ensure that cloud AI and edge AI are interoperable, enterprises can introduce cloud platforms alongside their edge applications to introduce a link between applications and services across the enterprise.

For instance, data can be processed at the edge, and edge AI can provide quick insights to users while using the cloud to process data to deliver longer-term insights to influence decision-making in various parts of the enterprise.

Additionally, where enterprises have use cases that require a hybrid of both edge and cloud AI, they can determine priority at-the-edge intelligence and non-time-sensitive intelligence. They can then build or adapt IT infrastructure to support the demands of both cloud and edge-based AI applications.

Training and Inference Algorithms in AI Applications

To use both edge and cloud AI effectively, enterprises need to understand the nature of the machine learning algorithms they consider for their use cases. They need to determine whether their approach relies on either training or inference algorithms.

The use of training algorithms means that the machine learning algorithms make predictions using their data feed as their training source. These algorithms increase in accuracy as they have a constant feedback loop to improve their performance and reduce error.

However, this approach may be computationally intensive, as these models require constant updating and revision as they continue to deliver results. This may introduce latency. The training approach ultimately becomes better suited for the cloud, which can satisfy its great computational requirements to provide accurate results.

An inference algorithm approach implements a trained algorithm to make predictions on the device where new data is received. As there is neither refinement nor a training loop, the inference system can easily be situated at the edge.

Benefits of Using Both Edge AI and Cloud AI

Improved Performance 

The cloud by itself is not ideal for AI applications. When edge and cloud AI complement each other, they can improve performance by boosting the sometimes partial processing that creates latency.

Distributed Learning

Edge computing helps reduce the load on the cloud. As such, edge devices can cooperatively train machine learning models locally as opposed to using a centralized training approach.

Better Decision-Making

Combining the real-time decision-making capabilities of edge AI and the compute and storage capabilities of the cloud that supports cloud-based AI, enterprises enjoy not only faster but smarter and more flexible decision-making.

Seamless Access to Data and Intelligence

A balance between cloud AI and edge AI ensures that an enterprise maximizes actionable insights and decision-making of both approaches. Data shared between edge and cloud models influences how machine learning models learn and how they are enhanced to provide enterprise-wide value.

The Drawback of Combining Cloud AI and Edge AI 

To get cloud AI and edge AI to work together, applications have to be designed to deliberately split and manage workloads between them.

Use Cases

Autonomous Vehicles

Edge and cloud AI technologies can combine to improve the performance of autonomous vehicles. Edge AI provides fast and accurate decision-making capabilities to ensure the vehicles make decisions in real time. This improves the identification of road and environmental elements to improve the safety, efficiency and overall autonomy of the vehicles. Additionally, edge AI ensures autonomous vehicles are more reliable in the face of connectivity challenges.

Cloud AI enables the reception of data from autonomous vehicles and, through machine learning and deep learning, assesses metrics such as the performance of the vehicles. This allows the on-board processing to be updated with improved driving capabilities derived from the data. The vehicles also enjoy access to the storage and compute capabilities of the cloud.

Edge-Enabled Cameras

Edge AI allows edge-enabled cameras to process information from their sensors without overburdening the network with insignificant data. Data can then be transmitted to the cloud for further analysis when the target objects are detected at the edge.

The Future of Edge AI and Cloud AI

The future of artificial intelligence applications may involve having to divide artificial intelligence into either cloud-native, edge-native, or hybrid applications. As more and more AI applications need faster real-time training, edge AI usage will continue to grow as an option for AI deployment.

Enterprises will increasingly seek fully integrated solutions in place of fragmented technology parts that they would have to assemble themselves. This would in turn drive technology partnerships to develop and pre-assemble complete solutions to provide to customers.

Hybrid cloud-edge AI architectures will become the norm as enterprises continue to realize that processing and decision-making should be carried out at both the cloud and the edge.

This UrIoTNews article is syndicated fromGoogle News

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