15 Edge AI Examples & Use Cases Driving Real-Time Innovation

Imagine a world where your devices think for themselves and make decisions independently. This is the potential of edge AI. By bringing AI processes closer to where data is being generated, edge AI helps to analyze and act on data with minimal latency. This blog will share practical edge AI examples that illustrate how this technology is already being used today to deliver faster, more efficient, and more intelligent AI systems.

One way to approach your edge AI project is to leverage AI inference APIs to reduce the heavy lifting of machine learning deployment. Inference’s solution can help you get to the good part of commercializing AI, applying intelligent models to solve real-world problems, faster than you ever thought possible.

What is Edge AI?

Edge AI is deploying AI models and algorithms directly on edge devices located at the network's periphery, close to where data is generated and actions must be taken. These devices encompass a wide range, from powerful edge servers to resource-constrained IoT sensors, and include familiar examples like:

• Smartphones
• Smart home appliances
• Autonomous vehicles
• Industrial robots

The Edge Advantage: Speed, Privacy, and Reliability

Recent advancements in AI, such as the development of smaller and more efficient language models like GPT-4o Mini, Llama 3.1 8B, and Gemma 2 2B, further accelerate the adoption of edge AI. This shift to edge AI offers several advantages:

• Speed: Decisions and actions happen in real-time, right where the data is generated. This is necessary for applications like self-driving cars or medical devices that can't afford delays caused by sending data to the cloud for processing.
• Privacy: Sensitive data can be analyzed locally without being sent to the cloud, enhancing security and privacy.
• Reliability: Edge AI reduces dependency on a constant internet connection, making it more reliable in areas with limited or unstable connectivity.
• Efficiency: Processing data locally reduces the bandwidth required to send everything to the cloud, saving energy and costs.

What are the Components That Make Edge AI Work?

To understand how edge AI works, we must understand its main components and processes.

Key Components

Edge AI comprises three main components:

• Edge devices
• AI models
• Communication

Diverse Edge Hardware

Edge devices encompass a wide range of hardware, from powerful servers capable of handling substantial computational loads to highly resource-constrained IoT sensors designed for specific tasks. These devices include:

• Smartphones
• Drones
• Autonomous vehicles
• Industrial robots
• Smart home devices

Some hardware manufacturers, like NVIDIA and Intel, even provide hardware support for deploying ML models on the edge.

Optimized AI Models for Edge

Edge AI employs various AI models, including machine learning, deep learning, and computer vision algorithms, optimized for efficient execution on edge devices. These models are tailored to operate within the constraints of edge environments, ensuring that they can perform effectively despite limited:

• Processing power
• Memory
• Storage

For instance, models like GPT-4o Mini and Llama 3.1 8B are designed to be lightweight and efficient, making them suitable for edge deployments.

Efficient Edge-Cloud Communication

Communication Protocols such as MQTT and REST APIs facilitate Efficient data exchange between edge devices and the cloud. These protocols enable seamless connectivity and data transfer, allowing synchronized operations between edge devices and central systems when necessary.

They also allow the transfer of information in compressed form in an almost lossless fashion to keep crucial details intact.

Lightweight Messaging for Constrained Networks

MQTT (Message Queuing Telemetry Transport) is a lightweight messaging protocol designed for constrained devices and low-bandwidth, high-latency, or unreliable networks. It uses a publish-subscribe model, allowing devices to send (publish) and receive (subscribe) messages without a direct connection.

This makes MQTT ideal for IoT applications where devices must communicate efficiently and reliably.

Stateless Architecture for Networked Applications

REST API (Representational State Transfer Application Programming Interface) is an architectural style for designing networked applications. It uses HTTP requests to access and use data. REST APIs are stateless, meaning each call from a client to a server must contain all the information the server needs to fulfill that request.

This makes REST APIs scalable and suitable for various web services and applications, including those involving edge devices.

What is the Process for Edge AI?

The workflow that powers edge AI involves three steps: data collection, data processing, and action.

Real-Time Information Gathering

Data collection Edge devices continuously collect data from sensors, cameras, or other sources, providing a steady stream of information. This data can range from environmental metrics and health parameters to video feeds and audio recordings, forming the basis for real-time analysis. A great example of data collection is how your smartwatch collects the number of steps you took today.

On-Device Insight Generation

Data processing AI models deployed on edge devices process the collected data locally. This step involves analyzing the data to:

• Extract meaningful insights
• Detect patterns
• Make predictions using AI models without relying on cloud resources.

Local processing ensures that decisions can be made quickly, such as a self-driving car determining which lane to choose in real-time.

Immediate On-Device Response

Real-Time Action Based on the AI model's output, edge devices can take immediate action. These actions include:

• Triggering alarms
• Adjusting the path
• Sending data to the cloud for further analysis

Acting in real-time is essential for scenarios requiring instant responses, such as security systems or medical devices.

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15 Edge AI Examples

1. Smart Speakers and Virtual Assistants

Edge AI enables the always-listening feature in smart speakers and virtual assistants like Amazon Alexa. This functionality allows the devices to run lightweight models locally to detect wake words like “Alexa.” Processing this data on the device instead of the cloud provides quick, real-time responsiveness, which is required for these functionalities to work successfully.

2. Wearables for Health and Fitness Tracking

Smartwatches and fitness trackers use Edge AI to monitor sleep patterns and physical activities. Many of these devices run simple models directly on the device. For example, with Edge AI, step counting or basic sleep tracking can be performed locally with fast and efficient feedback to the user.

Enhanced Patient Care with Localized Healthcare AI

Edge AI allows hospitals and other healthcare providers to reap the benefits of artificial intelligence without unnecessarily transmitting sensitive patient information. All the data collected from health monitoring devices like cardiac trackers and blood pressure sensors can be processed and analyzed locally. This enables real-time analytics that help medical professionals provide better patient care.

3. Automated Optical Inspection in Manufacturing

Edge AI supports defect detection in manufacturing processes. Smart cameras with Edge AI identify real-time issues like packaging errors or misaligned pallets.

4. Predictive Maintenance for Machinery

Edge AI analyzes sensor data like electrical current, vibrations, and sound to monitor machinery for potential issues. This data is processed locally to detect anomalies.

5. Autonomous Vehicles and Robotics

One example of Edge AI that everyone knows is a self-driving car. The car constantly collects data around it, which it then analyzes in real time. There is no time to move data back to the cloud since decisions must be made in the blink of an eye. Passenger aircraft have been highly automated for a long time.

Real-Time Analytics for Smarter Mobility

Real-time analysis of data collected from sensors can further improve flight safety. While fully autonomous and unmanned ships may not become a reality for years, modern ships already have extensive data analytics. For example, Edge AI technology can also be used to calculate passenger numbers and locate fast vehicles with extreme accuracy.

More accurate positioning is the first step in train traffic and a prerequisite for autonomous rail traffic.

6. Energy

A smart grid produces a vast amount of data. A brilliant grid enables demand elasticity, consumption monitoring and forecasting, renewable energy utilization, and decentralized energy production. Nevertheless, a smart grid requires communication between devices, and transferring data through a traditional cloud service is not the best option.

7. Retail

Large retail chains have been doing customer analytics for a long time. The analytics primarily analyzes completed purchases, i.e., receipt data. Although this method can achieve good results, the receipt data doesn’t tell you:

• How people move around the store
• How happy they are
• When they stop to watch something

Video analytics, which analyzes fully anonymized data extracted from a video image, provides an understanding of people’s purchasing behavior that can improve customer service and the overall shopping experience.

8. Predictive Maintenance

Predictive maintenance involves monitoring equipment condition and predicting potential failures before they occur. By analyzing sensor data from machines and applying AI algorithms, Edge AI systems can identify patterns and anomalies that indicate potential issues. This allows maintenance to be scheduled proactively and reduces the risk of unexpected downtime.

9. Quality Control

Another critical application of Edge AI in manufacturing is quality control. AI algorithms can be used to analyze images, sensor data, and other inputs to detect defects or deviations from the desired specifications. Manufacturers can achieve real-time feedback and adjust the production process by processing this data locally on edge devices, ensuring consistent product quality and reducing waste.

10. Secure Banking

Edge AI can auto-detect transactions triggered through different payment platforms in other countries or locally. It can also detect abnormal spending and other unusual activities. Edge AI will immediately send notifications to users asking them to confirm these transactions, offering users a great deal of financial protection.

11. Smart Content and Ad Services

Edge AI enables advertising companies to deliver tailored news and information to users. These innovative services show customers new products and services that will interest them.

12. Edge AI Delivery Logistics Systems

Edge AI can provide speedy tracking information to ensure packages arrive on time and at the right place. It also ensures that packages can be accurately tracked throughout their journeys.

13. Recommendation Engines

Edge AI-backed recommendations can provide users with content or product recommendations that they’ll want to see.

14. Automatically Generated Communications

With Edge AI, you can trigger birthday and holiday greetings automatically. You can also create photo and video albums to connect with loved ones.

15. Edge AI Enhanced Entertainment

Smart-features like in-context player information enrich and bring users closer to entertainment experiences, from football to movies.

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Key Steps in Developing Edge AI Applications

Define the Edge AI Application Problem Clearly

Choosing a specific problem to address with Edge AI applications is essential. For example, detecting manufacturing defects in real-time using camera-equipped IoT devices helps prevent the costly consequences of faulty products. This problem helps focus the project and define success criteria to guide development.

Collect Quality Datasets for Training AI Models

Collect high-quality datasets for training AI models. This data can come directly from target edge devices or simulated environments. For example, sensor data from machines can predict faults or monitor performance. In the case of manufacturing defect detection, images of defective and non-defective products will help the model learn to classify new input accurately.

Train the AI Model

Then use frameworks like TensorFlow or PyTorch to train machine learning or deep learning models. This process involves feeding the collected datasets into the chosen framework and allowing the model to learn to recognize patterns in the data.

Optimize Models for Edge Devices

Once you’ve trained a model and it’s performing well according to your success criteria, apply optimization techniques like quantization, pruning, and model compression to reduce its size and computational requirements. These techniques help the model run smoothly on edge devices with limited resources.

Select the Appropriate Edge AI Hardware

It is essential to choose the appropriate hardware for your Edge AI application. Depending on the AI workload, this could include microcontrollers, single-board computers, or specialized AI accelerators. For instance, lightweight models can run on microcontrollers with limited resources.

More complex models may require an edge device with an AI accelerator for efficient performance.

Deploy the AI Model to the Edge Device

Next, we must deploy the trained and optimized AI model to the edge devices. Once deployed, test for latency, accuracy, and reliability in real-world scenarios. Often, performance differs from test results obtained in controlled environments.

Connect the Application to the Cloud

To enhance the application, further connect it to cloud IoT platforms such as AWS IoT Greengrass, Azure IoT Edge, or Google IoT Core. These platforms support remote model updates, data storage, and scalability.

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