What is Edge AI?

SOURCE: Unsplash ( https://unsplash.com/photos/OSx6FqBtK7I )

It is no surprise that latency and full outage in Networks has become core concerns for Data Scientists and Organizations that actively use Artificial Intelligence to drive business decisions. Organizations and AI Engineers need to review existing IOT+AI pipeline architectures to ensure that they operate as intended in production environments.

Let’s take Autonomous Vehicles for example. As an ML Engineer building models for self-driving cars, you don’t want a situation where a self-driving car loses connectivity with the cloud environment and therefore is unable to make critical decisions in real-time(e.g. detect a child crossing the road); that will be disastrous.

Edge AI is the solution that bridges that gap by allowing organizations to utilize the power of AI while fixing potential problems that could arise due to network latency and full-fledged network outages.

So, what is Edge AI?

SOURCE: MediaTek (https://www.mediatek.com/blog/how-edge-ai-is-widely-changing-smart-devices)

It is a paradigm that employs various techniques to ensure that data collection, cleaning, and processing tasks are handled locally (i.e. either directly on the computing device or within a local server network closest to the device).

The main objective of Edge AI is to reduce and possibly eliminate bandwidth problems and associated costs while transferring and processing data over networks. Whether the hardware in question is a mobile phone, IoT device, self-driving car, or a manufacturing plant machine that checks the quality of products- the common need for real-time data processing in an AI deployment environment is met because Edge AI allows deployed AI models to operate reliably in real-time as compared to equivalent Cloud-based deployments.

In summary, Edge AI provides:

1. Data Security and privacy. With data transfer and preprocessing operations going on in a local network, this makes data leak/ bridge harder for an online attacker.
2. Resolves data latency, availability, and reliability. With Model preprocessing, training, and prediction operations executing locally, network-related challenges are minimized, and in most cases completely eliminated.
3. Scalability & Sustainability. The localization of AI Model deployment leads to the reliability of the system as the solution begins to scale up.

When to consider Edge AI & How to integrate Edge it into your business

If you are wondering if you should consider integrating Edge computing into your business operations, here are a few use cases that will benefit from Edge AI:

• Algorithms that require real-time analysis on mobile devices. such as Speech Processing or Computer Vision.
• Sensors and other IoT devices that handle tasks such as motion detection, object detection, etc.
• Software Applications, Cameras, and Machinery that monitor tasks such anomaly & fraud detection.

Setting up Edge Computing Environments

As an organization trying to adopt Edge Technology, setting up local computing infrastructures requires the following safety guidelines:

1. Ensure the Edge physical site should be a secure space to avoid malicious or accidental downtime incidents of IT equipment and unauthorized operations.
2. Provision of reliable power source (i.e. UPS, generator), maintenance, and distribution to the Hardware.

Limitations

Although Edge AI comes with a lot of advantages, it also has a few setbacks. It would be important to note a few of these disadvantages so that the management unit responsible for decision-making in the organization trying to adopt Edge computing is able to make a balanced evaluation of the risks and opportunities in relation to the organization’s priorities.

Edge devices need more hardware and software to handle heavy computation and local storage requirements. This means that as data grows, the organization will have to consider trading high-performing AI models for Hardware costs. So, while Edge AI solves the need for real-time data analysis, it lacks the computing power of a cloud-computing infrastructure. Also, when working edge devices, can introduce new complexities such as the need to synchronize data and knowledge base in local edge devices.

Choosing Hardware for Edge AI

Here are key points to note when choosing Hardware for Edge computing:

1. Hardware must be rugged enough to withstand deployment in volatile environments (vibrations, dust, extreme temperatures, etc).
2. Hardware needs to meet minimum real-time performance & storage requirements.
3. Hardware needs to be secure and also have a secure communication channel.
4. Hardware needs to meet minimum power utilization requirements

ML Tools/ Infrastructure beneficial for Edge Computing

Beyond just choosing the right hardware, there is a need for organizations to embrace workflow architectures that optimize the deployment and use of AI models in production environments.

1. Containerized applications. Containerized Software deployment is a form of virtualization where applications run in isolated user spaces, called containers while using the same shared operating system (OS) and Hardware resources. This architecture will allow edge devices to work with low bandwidth usage, low latency and give room for scalability.
2. Edge AI/ML optimized computing solutions. The development of ML libraries such as TensorFlow Lite, TinyML, and even already existing libraries such as Scikit Learn which are capable of building AI Models without much need for high computing resources is great for Edge computing. This has created room for efficient deployment of AI solutions to Edge devices.

Conclusion

Looking at AI products such as Alexa and other IoT devices, it has become clear how important Edge AI is in the world of Artificial Intelligence. There are numerous design architectures to work with and the hardware or services you choose will depend greatly on your needs and your budget. Edge computing is clearly the future of AI and organizations need to plan to integrate Edge AI into their AI solutions.