What is Edge AI?
Edge AI refers to leveraging artificial intelligence in applications that run at the edge, such as mobile, IoT, and custom embedded applications like interactive kiosks.
The concept combines AI processing with edge computing, a technical architecture that brings computing resources and data storage to the near side of the network, closer to the applications that consume it. By running AI and data processing locally, such as in a near-proximity data center or even on a mobile or IoT device, applications become faster and more reliable because they aren’t dependent on the internet for accessing data or AI models.
Because it provides superior speed and reliability, edge AI is suited to power applications such as smart homes, smart security, wearable devices, and autonomous vehicles, where low latency and 100% uptime are paramount.
Edge AI is becoming popular for delivering smart, secure, and hyper-personalized applications with the highest speed, availability, security, and efficient bandwidth. But because AI requires data, you need more than just the right models. You also need the right database to achieve the promise of edge AI.
Edge AI vs. Cloud AI
Cloud AI is a technical architecture where applications access data and AI models living on cloud infrastructure. This approach provides increased data storage and processing power for AI, is ideal for training and deploying more advanced models, and works well for apps with strong and consistent network connectivity. However, cloud computing presents challenges for apps running at the edge, mostly due to dependencies on an inherently unreliable internet. Because of the latency and availability issues common for modern networks, apps that require real-time results from AI models simply can’t achieve the speed and responsiveness to be effective.
Alternatively, edge AI processes data and AI closer to the point of interaction, where it’s most commonly created and consumed. With this approach, data and AI models interact with each other locally, even on-device, rather than in a distant cloud data center, removing dependencies on the internet.
Edge AI is the ideal option for applications where results are needed in real time. Consider an oil drilling platform at sea. Data is collected from sensors all over the platform as part of a daily routine, measuring pressure, temperature, wave height, and other factors affecting operating capacity. This kind of data comes fast, changes often, and requires a real-time response.
Suppose the oil platform’s data and AI models are stored and processed in the cloud. This means that captured data must be sent over the internet just to evaluate measurements with machine learning models.
Now, suppose a sensor on a critical platform component begins to detect signs of likely failure, a potential breakdown that could lead to a dangerous turn of events. It takes too long to collect data points on the component, send them to the cloud for storage and AI processing, and then wait for a recommended course of action. By the time the platform operators receive a response from the cloud, it might be too late.
Where seconds count, and the difference between uptime and downtime determines safety or disaster, depending on an unreliable internet connection isn’t an option. Edge AI reduces the risks by putting a data center on the oil drilling platform itself, solving the problems of latency and downtime. Instead of sending data to AI in the cloud, it’s processed locally – no more waiting on a slow connection for critical analysis.
Edge AI Benefits and Challenges
While there are many benefits of using edge AI architectures, there are also challenges you should consider. Here are some of them:
Benefits
Decreased Latency
By processing data and AI locally or on-device, you increase the speed of applications because data doesn’t have to travel to distant cloud data centers and back. And when you process on-device, you can achieve sub-millisecond response times.
Increased Availability
With local data and AI processing, you eliminate the risk of downtime due to internet or cloud data center outages, making app uptime more reliable.
Superior Data Privacy
Because edge AI stores and processes data and AI locally, you reduce the security risks of sending data over the internet and storing it in the cloud. Better governance and control over data at the edge ensures compliance with data privacy regulations.
Efficient Bandwidth Usage
Edge AI promises more efficient bandwidth usage by lessening the load between the cloud and the edge. This is important when using shared networks, where bandwidth can vary greatly depending on time and demand.
Real-time AI
Because data and models are local, hyper-fast responsiveness is guaranteed.
Challenges
Data Storage and Processing
Edge AI requires a database that can be deployed across the entire architecture, including on-device. Because of this, organizations need to look for database solutions that offer both server and embedded versions of the database. Additionally, the database should offer vector search capabilities and the ability to call AI models directly. Using a database that combines these features will save you time and effort when developing your edge AI applications.
Data Synchronization
Data synchronization is required in a distributed application to maintain consistency and integrity. Organizations must ensure that data changes are instantly reflected across the app ecosystem and that write conflicts are handled correctly.
Model Size
Many LLMs are large and require the horsepower that comes with cloud computing, making them unfeasible for edge AI use cases. However, a growing number of lightweight embedded AI models are emerging in the market that are optimized for running on mobile and IoT devices. The tradeoff typically comes in precision: the lightweight models may be less precise than their cloud-sized counterparts. However, in many cases, the tradeoff is worth it for the real-time performance and security you get from local AI processing.
Edge AI Use Cases and Examples
Edge AI has the ability to power a multitude of innovative applications. Here are a few examples:
Security Systems
Images captured from live camera feeds can provide valuable surveillance in secure locations, but only if the AI evaluating the images performs fast enough. By processing and evaluating image data locally, you gain the speed required for real-time image analysis, allowing instant action when incidents occur.
Smart Stores
Next-gen retail promises AR/VR-based immersive applications designed to enhance the customer experience, such as smart mirrors showing customers how they would look in various styles and colors of clothing or recommending an accessory to go with something they try on. There are also cameras and scanners that track customers as they move through the store, which can enable personalized recommendations or smart shelves that record customer item selections and transact the purchase as they leave. These sorts of experiences require the immediacy of edge AI to make a true impact on customers.
Manufacturing
Industrial IoT, or Industry 4.0, is a key driver of edge AI. For example, sensor data from equipment on a factory assembly line is acquired and analyzed locally in factory data centers to make real-time predictions of likely outcomes for real-time alerting and preventative maintenance.
Healthcare
With edge AI, hospitals can process patient monitoring data locally and provide real-time diagnosis while adhering to data privacy regulations. Similarly, ambulances and medevac helicopters can eliminate dependencies on the internet, allowing EMT staff to administer care while en route, saving precious seconds and bettering patient outcomes.
Couchbase for Cloud-to-Edge AI
Couchbase natively supports edge AI architectures by providing the following:
A Cloud-Native Database: Couchbase Capella supports vector search and includes columnar services, a columnar data store for running complex analytic queries against operational data without any impact on operational workloads and without the need to move data into a separate repository for analysis. It’s great for real-time operational analytics and can be part of a data pipeline for ML training iterations. The columnar service can also call fully-trained ML models via a user-defined function (UDF) feature.
An Embedded Database: Couchbase Lite is the embeddable version of Couchbase for mobile and IoT apps that stores data locally on the device. It provides full CRUD and SQL query functionality and support for vector search and predictive queries for calling AI models at the edge.
Data Synchronization from the Cloud to the Edge: This is a secure, hierarchical gateway for data sync over the web and peer-to-peer sync between devices. It supports authentication, authorization, and fine-grained access control. Choose from fully hosted and managed data sync with Capella App Services or install and manage Sync Gateway yourself.
Learn more about Couchbase’s capabilities for cloud-to-edge AI and vector search in this blog post.