Edge computing brings data processing and storage closer to the devices that use and gather the data so that users are not tied to a central location. Edge computing enables real-time data usage and processing without the latency, bandwidth, and security issues common with traditional, centralized data infrastructures.
Edge artificial intelligence (AI), involves running machine learning (ML) algorithms on a device (such as the popular TinyML framework for microcontrollers in edge devices.) This allows for fast performance, adherence to the same data privacy standards as in-network devices, and efficient power consumption. Edge AI also helps companies maximize operational efficiency.
This guide examines deployment models for edge AI, discusses how to implement AI at the edge, and explains how to choose a deployment model.
Deployment Models for Edge AI
There are four main deployment models for edge AI.
In an on-device deployment model, edge AI operates locally on edge devices without needing cloud-based or other external resources. This method of edge AI deployment is ideal for use cases like autonomous vehicles, machinery, and IoT devices that necessitate on the spot insights generation and very low latency.
Through an Edge Server
In an edge server deployment model, AI technologies operate on special edge servers close to the edge devices. Devices send data to the servers, the servers process the data, and actionable insights are delivered to the devices. This model is ideal for applications that require more processing power than individual devices can provide or in scenarios that require data from multiple devices to be processed simultaneously.
In a cloud-based deployment, AI operations occur in the cloud. Edge devices collect data and transmit it to the cloud. The data is processed, analyzed in the cloud, and then actionable insights are delivered to edge devices. The cloud-based approach to AI deployment is ideal for complex scenarios that demand rapid scalability, extensive processing power, and higher than average data privacy standards.
A hybrid deployment model utilizes both edge server and on-device solutions. Select AI activities occur on individual devices; demanding and complex AI tasks are performed on edge servers or the cloud. The hybrid deployment model is ideal for scenarios with many different types of devices or applications with varying workloads.
How to Implement AI at the Edge
Implementing AI at the edge involves tailoring standard implementation processes for the execution of AI algorithms and models within an edge infrastructure. Here is an overview of 12 steps to implement edge AI:
- Determine the Objectives - Identify and define the task or problem edge AI must address. List the goals, requirements, and parameters of the application.
- Identify the Users - Identify and describe the devices, machines, people, and systems that will use the edge AI solutions.
- Collect Data - Gather the data, such as sensor information, images, or other inputs, to train the AI model.
- Preprocess the Data - Prepare the data that goes in the AI model. Preprocessing commonly includes functions such as scaling, normalizing, and feature extraction.
- Determine the AI Model - Develop a custom AI model or select an existing AI model that matches the established objectives, requirements, and use case.
- Optimize the AI Model - AI devices typically have limited resources. Refine the AI model with techniques such as model quantization, compression, and pruning to prepare the model for operation on edge devices.
- Convert the AI Model - Edge devices are built with specific hardware and software. Convert the AI model to be compatible with and optimized for the target devices.
- Prepare the Devices - Adjust the devices’ operating systems, firmware, or applications to support AI execution.
- Integrate the AI Model into the Edge Devices - Integrate the AI model into the devices’ software.
- Test the AI Model - Test the AI model’s performance, responsiveness, and accuracy on the devices. Compare the results to the established goals and change the model and implementation as needed.
- Deploy the AI Model - Deploy the AI model to the edge devices.
- Monitor, Update, and Maintain the AI Model - Automate monitoring mechanisms to track and collect data on the resource usage, performance, and potential issues of the AI models on the devices. Collect user feedback, data, and performance metrics. Conduct routine updates and maintenance and continually enhance the edge AI solution.
Choosing a Deployment Method for Edge AI
To choose a deployment method for edge AI, review the following factors:
- Processing Power - If the application demands high processing power and few resources are available on-device, consider a cloud or server approach.
- Latency Requirements - Consider on-device deployment for applications that demand the lowest latency.
- Privacy Requirements - Consider edge server or cloud deployments for applications with strict privacy requirements.
Companies can partner with Encora to demystify Edge AI. Encora’s deep expertise in various disciplines, tools, and technologies power the emerging economy. This is one of the primary reasons clients trust Encora to lead the entire Product Development Lifecycle. Contact us to learn more about Edge AI and our software engineering capabilities.