What is edge AI?

Edge AI is a technology that combines artificial intelligence algorithms with edge computing to enable instant, low-latency AI processing on local devices without the need for cloud communication.

Edge AI vs cloud AI vs distributed AI

Appreciating the differences between the most common AI deployment strategies is critical for optimizing performance and safeguarding privacy. Cloud AI, edge AI, and distributed AI, have their own unique strengths and use cases, making it essential to align the method with your specific goals.

Here's how the three AI methods compare to each other:

Cloud AI

Cloud AI uses online computing platforms to provide access to advanced computational processing without needing expensive on-site hardware.

Key characteristics:

Data transfer is slower than local storage.
All data gets stored in centralized servers.
The availability of cloud AI ultimately depends on having a reliable and stable network connection.

Distributed AI

Distributed AI divides processing tasks across interconnected devices, enabling collaborative and resilient computation. This decentralized model enhances scalability and fault tolerance, making it well-suited for complex systems with diverse processing needs.

Key characteristics:

Multiple computational endpoints mean many different devices work together to process information.
With collaborative processing, each device in the network handles a small part of the bigger task.
Due to flexible resource allocation, the network shifts computing power and tasks between devices as needed. If one computer fails, backups take over the job to prevent disruption.
Advanced coordination methods help organize computers to complete tasks.

Edge AI

Edge AI uses local computers and devices to complete processing tasks at the data source rather than in massive remote server farms.

Key characteristics:

The processing happens on the devices where the data is collected.
Only the essential data or results are sent over the network, rather than all the raw data.
There is very low latency since no time is lost when sending data to distant servers before processing. The local devices can make instant decisions based on the situation.
Localized data processing means less sharing of private information across systems.

What are the benefits of edge AI for your business?

Artificial intelligence at the edge is transforming how businesses operate, offering significant opportunities to enhance efficiency and secure a competitive advantage. The fact that so many organizations recognize the advantages of edge AI means that the global edge AI market, valued at approximately $14.79 billion in 2022, is projected to grow at a compound annual growth rate (CAGR) of 21% from 2023 to 2030.

Some of the benefits edge AI offers include:

Lower latency: Shortens the time it takes to make decisions by processing data at source.
Reduced bandwidth consumption: Since data is processed locally, edge AI lowers the bandwidth needed to transfer data.
Stronger data privacy: With local processing and minimal data transfer, user information is rarely exposed to potential hackers.
Increased reliability: Edge AI is reliable and works even when the internet connection is unstable.
Decreased infrastructure costs: Less hardware is needed to move and store data, reducing costs.
Up-to-date analytics: With processing done at source, instant analysis delivers insights based on the very latest data.
Robust security protocols: The technology uses advanced cybersecurity standards like DDoS protection to block data breach attempts.

How does edge AI work?

Edge AI uses advanced computational techniques to enable direct, localized, intelligent processing on local devices. This approach reduces reliance on cloud infrastructure, enabling faster responses, lower bandwidth usage, and enhanced data privacy.

Here's an overview of how edge AI works:

Data collection: The system aggregates data from local devices and sensors.
Preprocessing: The raw data is cleaned, organized, and formatted for analysis. This step eliminates errors and ensures that the data is ready for computational interpretation.
Neural network processing: The preprocessed data goes into a neural network. The network uses machine learning techniques to look through the data to identify patterns and relationships.
Decision generation: Based on the analysis, the system produces actionable insights, predictions, or decisions without external communication.
Continuous learning: Edge AI systems iteratively update and refine their models through incremental local training, improving their intelligence and decision-making over time.

What are the use cases of Edge AI?

Edge AI enables devices to process AI tasks locally, avoiding the extra resources involved in sending data to distant servers. By analyzing data closer to its source, edge AI delivers faster insights and responses while reducing bandwidth and promoting sustainability. This approach increases productivity across diverse industries.

Here are some key use cases for edge AI:

Healthcare: The technology analyzes medical data at the point of care, delivering rapid diagnoses and treatment recommendations without relying on cloud connectivity.
Manufacturing: Sensors on production lines detect defects or anomalies instantly. Predictive maintenance systems minimize downtime and improve operational flow.
Security surveillance: Tasks like video analysis, license plate recognition, and facial identification run directly on devices, enabling faster responses and reduced dependency on cloud resources.
Retail: Smart shelves track inventory, identify misplaced items, and optimize store layouts to help improve the shopping experience.
Autonomous vehicles: Self-driving cars rely on edge AI to process sensor data, detect obstacles, and make navigation decisions immediately.
Smart homes: Devices in connected homes use edge AI for voice commands, personalized settings, and adaptive security systems. Local processing ensures functionality even without internet access, while preserving user privacy.
Smart cities: Traffic management systems, public safety measures, and energy grids operate using distributed sensors to optimize urban infrastructure.
Agriculture: On tractors, drones, and agricultural robots, edge AI analyzes crop health and soil conditions, delivering actionable insights directly to farmers, even in remote areas without reliable connectivity.
Financial services: Banks use locally processed data to detect fraudulent activity, provide personalized advice, and accelerate loan approvals.

Cloud computing's role in edge AI

Combining cloud infrastructure and edge AI creates a balanced system where instant responses meet scalable, centralized oversight. While local devices handle immediate tasks, the cloud supports broader data processing and long-term improvements.

Here's how cloud services complement edge AI:

Initial model training: Cloud infrastructure processes large datasets to build and refine AI and machine learning models before deploying them to the edge.
Periodic model updates: Cloud platforms ensure deployed models remain accurate and effective through regular updates.
Comprehensive analytics: Aggregated data from multiple edge deployments is centralized in the cloud, enabling better decision-making.
Backup and redundancy: Cloud systems store copies of AI models and data, ensuring quick recovery if edge servers experience issues.
Complex computational tasks: For resource-intensive tasks beyond the capacity of edge servers, cloud systems handle data processing and relay results back to edge devices efficiently.

Make the most of edge AI with Fastly Compute

Artificial intelligence at the edge enhances technological ecosystems by ensuring more efficient, intelligent, and adaptable systems. It eliminates delays by processing data closer to its source, reducing resource consumption, and improving operations across industries.

Fastly Edge Compute empowers your business to deploy AI at the edge, closer to your customers. This capability supports faster, personalized experiences while improving efficiency. Key features and benefits include:

Instant data access: Retrieve data from the Fastly KV Store in milliseconds for quick decision-making.
Global expandability: Fastly's multi-terabit-per-second network scales to meet your demands while maintaining top performance.
Easy deployment: Setting up and deploying Fastly Edge Compute is straightforward for IT staff and doesn't need complex configuration.
Improved personalization: The system provides customization options to tailor the experiences for each end user.
Secure by design: Security features like WebAssembly are built into the core system architecture and platform by default.
Simplified developer tools: The tools provided to developers are simple to speed up building applications.
High-speed messaging: Communication protocols like Fastly Fanout enable sending messages instantly between system components.
Improved performance: Fastly Edge Compute's processing is finely tuned to maintain maximum performance standards even under high loads.
Comprehensive observability: The platform's observability tools provide insight into all processes, transactions, metrics, and logs throughout the full system stack.