Cloud AI vs. Edge AI: know their differences and choose the right approach for your AI project
As we discussed in a previous article, Edge AI and Cloud AI are two different approaches to implementing Artificial Intelligence developments or machine learning models.
In a nutshell,
- Cloud AI stores and processes data on Cloud platforms or servers, where AI algorithms and models are executed.
- Edge AI captures or receives data and runs AI algorithms and models on local devices such as wearables, IoT devices, or Edge Computing servers.
Since each approach has its advantages and disadvantages, the choice between the two models will depend on the project’s needs and requirements, context, environment, and even location.
Edge AI advantages
In Edge AI: Artificial Intelligence outside Cloud, we saw that even in environments with poor coverage or no connectivity, Edge AI can make decisions, execute instructions, or provide real-time insights or responses. This is especially crucial for critical IoT solutions in industries like manufacturing (IIoT), healthcare (IoMT), or mobility.
This is made possible because Edge devices can capture, process, and analyze data locally, close to where the data is generated or needed. For example, in factories, offices, hospitals, or farms, without the need to send large amounts of information to remote servers or cloud platforms. It can even function without relying on a permanent broadband connection or low latency.
Additionally, Edge AI offers additional advantages:
- By processing and storing data on the device, the risk of interception or storage by third parties is reduced. It also reduces exposure during transmission or storage. This often provides greater control over the data and makes it easier to comply with local regulations regarding data protection, residency, or privacy.
- Operational cost savings, as only the most relevant or already processed data is sent to the cloud. This typically requires less computing power and storage space in the cloud.
- Edge AI devices can be configured and programmed according to the project’s needs at a relatively low cost, allowing for customization and adaptation to the solution to make them more efficient.
Edge AI disadvantages
Edge AI devices have limited data processing and storage capacity. Some tactics, such as algorithm optimization or the use of Application-Specific Integrated Circuits (ASICs) applied to Artificial Intelligence, can improve their performance. However, their capacity is still limited compared to cloud platforms’ virtually unlimited resources.
This characteristic can limit the complexity of machine learning models and Artificial Intelligence algorithms that can be executed on Edge AI devices. This is particularly relevant to devices like wearables or IoT sensors that operate on battery power. Algorithm execution consumes significant energy, diminishing autonomy.
Additionally, it’s worthwhile to consider that:
- Incorporating Artificial Intelligence or machine learning capabilities into specifically designed devices can result in higher hardware costs than equivalent devices or IoT sensors without this capability.
- For the same reason, these devices often require more maintenance, both at the hardware and software levels, to ensure they remain updated and operational.
- Although storing and processing data locally has advantages, Edge AI devices are also vulnerable to security or privacy breaches and attacks, exposing their data to potential attackers. Encryption or obfuscation can help protect Edge AI data.
Cloud AI advantages
Scalability, capacity, and accessibility are some of the main attributes and advantages of the Cloud. Cloud AI is capable of processing and storing large amounts of data, even massive volumes, adapting to the needs and demands.
Cloud AI also:
- Often, they utilize well-known platforms with abundant training and informational resources. This streamlines the deployment of Artificial Intelligence models through intuitive interfaces and training and deployment tools that simplify implementation.
- For this reason, implementing Cloud-based Artificial Intelligence solutions can be simpler and cheaper than implementing Edge AI solutions on proprietary hardware. The cloud allows companies to pay only for the resources they need (pay-as-you-go), saving costs on infrastructure, maintenance, and personnel.
- It is accessible from anywhere and at any time, as long as there is an internet connection. This enables remote and real-time access to processed data, facilitating integration with other existing applications and business processes.
- Additionally, automatic hardware and software updates provided by Cloud providers ensure that Cloud AI solutions run on up-to-date platforms with the latest security updates.
Cloud AI disadvantages
- Availability and latency (the time it takes for data to be sent and received between the device and the cloud) can be problematic for applications that require real-time response.
- Data processing and machine learning consume high resources, increasing operational costs.
- Sending data and information to Cloud platforms can pose risks, regulatory non-compliance, and even data exposure, especially when dealing with sensitive data transferred to third-party Cloud platforms in cross-border regions.
- Cloud AI applications require a robust and capable internet connection. This can be a hurdle in environments or areas with limited connectivity, low bandwidth, or no connection or coverage.
Differences in Security: Risks and Challenges
As we have seen, both approaches entail risks and challenges regarding data security.
Edge AI is worth noting for the following reasons:
- Edge devices, being located in accessible physical environments, may be more prone to physical attacks, including theft, breakage, or tampering.
- Edge devices are also vulnerable to interceptor attacks during data transmission or storage on the device.
- The diversity and quantity of Edge devices and proprietary developments can make security updates challenging, leaving them vulnerable to threats.
- Limitations of Edge devices can also hinder sophisticated security measures, like end-to-end encryption or biometric identification.
Risks and security challenges in Cloud AI:
- Cloud service providers implement appropriate security measures to prevent data theft, leaks, or security breaches.
- In some cases, sensitive data may be subject to specific regulations regarding data location and residency. If cloud providers fail to comply with these regulations, they risk regulatory non-compliance.
- When using third-party cloud services, data protection policies and privacy regarding data protection depend on the Cloud provider.
- The time required to transmit data to and from the cloud (latency) can create delays, which can be problematic for applications requiring real-time responses. Additionally, lack of connectivity or interruptions can affect cloud services availability and access.
When is Edge AI more suitable? When is Cloud AI better?
The choice between Edge AI and Cloud AI depends on the specific use case. This will depend on factors such as connectivity availability, scalability, or data sensitivity.
Edge AI is highly suitable for situations where real-time response is required or network connectivity is limited.
- In industrial environments, Edge AI allows for real-time processing of sensor data without latency, ensuring no interference with the production process.
- In healthcare, Edge AI can process patient information without medical data leaving hospital premises. This provides an immediate response that is vital in critical situations.
- Self-driving robots, autonomous vehicles, drones, or AGVs (Automatic Guided Vehicles).
On the other hand, Cloud AI is more suitable in situations where scalability is essential to handle large volumes of data.
- In logistics or e-commerce companies, Cloud AI enables efficient processing and analysis of vast amounts of information, including user data, customer data, and transactions.
- Banking risk analysis and fraud detection require processing large amounts of data to identify complex patterns. This is done to detect suspicious or fraudulent operations.
- In services like automatic translation or voice recognition, Cloud AI can perform large-scale language analysis and understanding, improving response accuracy and quality.
Some applications will require immediate response and less dependence on network connectivity, while others will require extensive processing and data analysis capabilities.
Ethical considerations: a common need for both approaches
Both Edge AI and Cloud AI require careful consideration of data security and privacy.
- In the case of Edge AI, it is imperative to ensure that data is adequately protected on Edge devices and that there are no risks of privacy breaches or unauthorized access.
- In the case of Cloud AI, it is necessary to verify that Cloud service providers have appropriate security policies and measures in place to protect data and comply with data protection regulations.
In both cases, Artificial Intelligence development and models should avoid biases and discrimination. This can happen because the data used to train the models may be biased, or because the algorithms themselves unintentionally introduce biases.
To mitigate biases and discrimination, it is necessary to conduct testing and evaluations that identify and address potential biases. This ensures that AI systems are fair and unbiased.
Furthermore, Artificial Intelligence implementation, whether in Edge AI devices or Cloud AI, requires careful consideration of responsibility and accountability. It is critical to clearly define who is responsible for AI systems’ outcomes and functioning, and to establish mechanisms to address any issues or consequences that may arise.
Lastly, it is always critical to understand and explain how AI models work, how they make decisions, and how they generate results. Therefore, ensuring transparency and explainability of the algorithms and AI models used is essential, particularly in critical domains such as healthcare.
Artificial Intelligence ethical considerations should be comprehensively and carefully addressed throughout the entire development and implementation process. This is regardless of whether the model is applied using Edge AI or Cloud AI approach. The goal is to ensure that AI models are fair, transparent, explainable, responsible, and respectful of privacy and individual rights.
More articles in this series:
- Raspberry Pi for Edge AI: Artificial Intelligence at the Edge for Everyone.
- Edge AI: Artificial Intelligence outside Cloud.
Featured photo: DilokaStudio on Freepik.