Edge computing is a crucial aspect of computer vision that allows data processing to take place at the edge of a network, closer to the data source, rather than in the cloud. Placing computation and storage close to where data is generated saves bandwidth and improves response times. The rising demand for real-time processing and low-latency applications, thus makes edge computing increasingly relevant. The Edge Computing market, which is currently at $44.7 billion is projected to grow to $101.3 billion by 2027. With the advent of powerful edge devices like smartphones and tablets and the emergence of ultra-high-speed 5G networks, edge computing is becoming a reality and has led enterprises to think beyond just data centers and cloud systems for computing.
Edge-based Computer Vision: Why is cloud not enough?
Cloud computing technology enables businesses to scale at an unprecedented rate. However, there has always been skepticism around data security on the cloud. Sometimes, cloud service providers fail to take appropriate regulatory and data protection measures, resulting in a heavy cost of data security for enterprises. Though, as a disruptive technology, cloud computing offers businesses several benefits such as flexible and low maintenance IT infrastructure, elastic scalability, and pay-per-use models, they also have several disadvantages such as unpredictable downtimes, security, and privacy concerns, vulnerability to attacks, and limited control. The bandwidth, speed, and security concerns of cloud computing have forced enterprises that deal with the processing of computer vision data to turn toward edge computing. Edge AI solutions facilitate better-automated processes that provide real-time actionable insights. If we look deeper, edge computing is becoming critical for computer vision operations due to its ability to deliver faster responses and stronger security than other cloud computing platforms. In addition, edge computing has lower bandwidth demands as the computation is close to the source of data.
Why Should Enterprises Opt for Edge Computing?
- Enhanced speed/lower latency
By definition and design, edge computing eliminates the need to move data from endpoints to the cloud and the computation result from the cloud to the endpoints. These reduced transfer costs result in time savings of the order of milliseconds and sometimes even seconds. It may not seem like a big deal, but in a connected world where real-time decision-making capabilities are required for endpoints to function properly, travel time (called latency) is important. For example, autonomous vehicles, industrial and manufacturing IoT deployments, and medical applications all require machines to analyze data and respond nearly instantly.
- Robust Security and Privacy protection
Edge computing can improve security and privacy by keeping data closer to the edge, away from centralized servers. Edge devices are still vulnerable to hacking attacks, especially if not adequately protected. However, the amount of data stored in edge devices is very limited and often not a complete dataset and hence not of much interest to hackers. On the other hand, endpoint data stored on central servers is typically combined with other data points to create a more comprehensive collection of information that hackers can use for malicious purposes. For example, consider edge computing in healthcare. Sensors capture the patient’s vital signs, which an edge device analyzes. This device retains only these measurements. However, if an endpoint sensor sends data back to a central server where it is stored along with other information, including personally identifiable information about a patient, and that information is hacked, the patient’s privacy is at risk.
- Operational cost savings/reduction
While the cost of storing data has dropped significantly over the past decade, the cost of moving data has increased as volumes have grown. Experts predict that connectivity costs will continue to rise as data volumes grow. We also expect users to use more bandwidth to handle the load, increasing the price.
Edge computing can keep costs down, or at least as low as possible, by reducing the amount of data sent to and from the cloud.
- Reliability and resiliency
Edge computing continues functioning even when communication channels are slow, intermittently available, or temporarily down. For example, an energy company deploying edge computing on its oil rigs does not need to constantly rely on available satellite links to send data back to its data center for processing. Instead, you can only transfer the necessary processed information from the edge back to your data center when connectivity becomes available.
By reducing a central point of failure, edge computing enhances resiliency. Unlike centralized servers, a failure at one edge device will not affect the performance of other edge devices in an ecosystem, improving the overall reliability of the connected environment.
Similar to cloud computing, organizations can scale their edge devices as their uses grow. This elasticity allows them to deploy and manage only as many devices as needed. Additionally, endpoint hardware and edge devices often cost less than adding compute resources into a centralized data center, allowing organizations to scale more efficiently and cost-effectively at the edge.
AI is set to transform enterprises at the edge
Productive Edge AI Models run on advanced infrastructure which is designed for edge computing, allowing you to handle heavy AI workloads at the edge of your network. Edge AI brings real-time analytics to enterprise data processing. This is why countless global companies are embracing the power of Edge AI.
With 5G technology now available in most countries, edge AI will be amplified as more industrial applications emerge. Given that Insight calculates an estimated 5.7% ROI (return on investment) from industrial edge AI deployments over the next three years, it makes sense to invest in edge AI in your business.
Edge Computing and Edge AI transform the future of computing
With worldwide spending on Edge Computing and Edge AI already so high and the increased usage rate climbing daily, edge Computing use cases are expected to grow exponentially every week throughout the remainder of the year. Many companies and industries are already using edge computing and AI together, and it’s only a matter of time before more companies fully integrate them. Eventually, traditional computing methods will be pushed aside in favor of edge computing, as edge AI’s cost-effectiveness and improved security have already transformed computing and made the Edge more popular. By 2025, it is estimated that 75% of enterprise data will be generated, processed, and analyzed at the edge, thanks to Edge AI.
Edited By: Naga Vydyanathan