The intelligent edge is an on-premises system that collects, processes and acts upon data. Typically used within Internet of Things (IoT) arrays, the intelligent edge uses edge computing to reduce response times, bandwidth needs and security risks. Since all the actions are taken on premises, the data does not need to be sent to the cloud or a data center to be processed.
The intelligent edge offers a faster route to turning data into helpful information since it’s implemented across dispersed environments. This type of system can help improve service delivery and product development.
Many industries are adopting the intelligent edge, including:
Today, IoT arrays are used to support decision-making — sending data to a cloud platform for analysis and visualization to inform staff. While this model helps improve response times and increase efficiency, intelligent edge IoT accelerates these benefits as they work more autonomously than standalone sensors that transmit information.
Since intelligent IoT both records, processes and addresses data on-site, business agility is greatly improved. This benefits cloud and infrastructure utilization, as an organization only uses what’s required over what might be needed.
Any intelligence that can be gathered by processing data closer its origin is considered intelligent edge — but not all edge intelligence is artificial intelligence. Edge AI falls under the umbrella of intelligent edge, but specifically refers to running AI or Machine Learning (ML) models on an edge device.
There are many benefits to edge AI. Reduced latency in edge computing helps ML models to analyze data and deliver insights in near real time. This accelerated performance is increasingly critical as AI plays a more integral role in everyday business functions. Edge computing also improves the reliability of AI systems by reducing service interruptions associated with transferring data from the cloud to the point of delivery. This provides a better user experience for employees and customers who depend on the intelligence these systems deliver.
Many AI solutions rely on personal or business-critical data to execute tasks. Edge technology largely eliminates privacy or compliance concerns by allowing data to be more securely processed or stored at the edge, rather than in the cloud. This can also reduce bandwidth and capacity limitations, as well as bring down operating costs.
Today, many edge devices have the ability to run small AI models independent of the cloud. Over the next few years, advances in edge technology will enable businesses to run more powerful and accurate models on smaller edge devices, allowing AI to be deployed more rapidly and cost-effectively.
Organizations that embrace intelligent edge — and more specifically, edge AI — gain a critical advantage in driving growth and profitability. But becoming an intelligence-driven organization requires an advanced level of technical maturity and agility across the IT-business divide. The ability to develop and operationalize the edge depends on three key factors.
First, deploying edge devices at the point of use results in an increasingly dispersed IT environment. Since most distributed locations will not have an IT team on-site, a secure endpoint management solution is required to ensure systems perform as expected. This enables devices to be remotely monitored, operating systems to be patched, applications to be updated, and security or storage issues to be identified. Secure endpoint management makes this possible at scale through a single solution or service.
Intelligent edge solutions often rely on cloud applications to process and deliver data to users. Continuously managing, updating and deploying these applications at scale requires an automated lifecycle, enabled through DevOps. DevOps provides the framework to support continuous feedback loops, the development and testing of new application features, as well as the deployment of new features to the staging and production environments.
Similar to DevOps, MLOps supports the operationalization of edge AI by automating the machine learning lifecycle. This framework is essential to ensuring AI solutions remain accurate and effective, even as a business environment changes. MLOps goes beyond traditional DevOps to provide a structure for continuously training, testing, deploying and re-evaluating ML models to maximize long-term value.
With all three of these elements in place, organizations can more easily manage and scale their intelligent edge solutions.