6 justifications for operating artificial intelligence on the periphery

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Artificial intelligence (AI), machine learning (ML), and edge computing are terms commonly encountered nowadays. But what if we merge these ideas to create AI at the edge? The advantages of deploying AI in distributed edge computing settings are vast for sectors like manufacturing, retail, transportation, logistics, smart cities, and more. The possibilities are endless! This article delves into several rationales for embracing AI at the edge.

Understanding Edge Data

Edge data comprises data generated from IoT devices, user interactions, or models operating locally on edge devices. It excludes the notion of training extensive models on a centralized cloud platform.

Enhanced Decision Speed

Deploying AI inference at the edge offers a major advantage – the ability to act swiftly. With computational power situated near the data source or end-user, latency in transmitting user data to a central server or cloud is minimized. This enables near real-time decision-making, leading to prompt responses and increased operational efficiency. For instance, in manufacturing, AI at the edge aids in identifying machinery irregularities and initiating maintenance alerts before catastrophic breakdowns occur.

Decreased Data Transmission

Another benefit of employing AI at the edge is the reduction in necessary data transfer when processing data locally. Processing data at the edge through inferencing lowers the volume of data requiring transmission over networks, freeing up bandwidth for critical applications. This reduction in data transfer is particularly crucial in industries like transportation and logistics, where vast amounts of data are generated from sensors and cameras.

Operational Continuity During Network Disruptions

An underrated advantage of utilizing AI at the edge is the capability to function even when network connectivity is disrupted. This ensures that essential operations can continue during network outages. For instance, in smart offices, AI at the edge can help manage critical building systems during connectivity lapses. Similarly, in logistics, AI at the edge aids in tracking and monitoring shipments in remote regions independently of network connections.

Isolated Environments

Occasionally, applications or data are too sensitive to be connected to the internet, or they are situated in extremely remote locations. Implementing AI at the edge offers the advantage of speed and insights from localized models in disconnected setups. For instance, a secluded station seeking new energy sources may necessitate potent on-site computing but lacks connectivity to a cloud. Local hardware can swiftly run machine learning models to expedite processes.

Data Sovereignty

Using AI at the edge plays a role in ensuring data sovereignty. By keeping data where it originated, the risk of interception or misuse decreases. This is especially vital in fields such as healthcare and finance, involving sensitive information. For instance, in the public sector, retaining data within a local facility ensures it stays clear of hostile regions compared to uploading it to a sprawling public cloud that transcends boundaries (while implementing encryption and other security measures).

Enhanced User Experience and Expanded Application Capabilities

Having computational resources right at the user’s location enables new applications and enriches user experiences. Imagine visiting a retail outlet where AI at the edge analyzes customer behavior to offer personalized recommendations, enhancing the shopping journey. AI at the edge not only accelerates application responses but also maximizes opportunities with real-time predictions at the same location where the user is, providing suggestions tailored to specific events or understanding customer preferences based on past purchases.

Compact Designs and Edge Applications

Red Hat provides various OpenShift topologies to cater to different edge deployments. Starting with 3-node clusters for high availability in compact forms, these configurations offer options for remote worker nodes and single-node setups in remote locations. Red Hat OpenShift AI extends model serving capabilities to the edge, facilitating the deployment of machine learning models in resource-constrained and intermittently connected environments.

For more compact setups, Red Hat Device Edge offers even smaller configurations, allowing for applications to run efficiently in minimal hardware configurations. Regardless of the chosen topology, the provision of diverse options ensures optimal resource allocation tailored to application needs.

Extending beyond centralized data centers to embrace distributed systems has been a popular choice for years, leading to the creation of self-contained and independently deployable decentralized architectures. This paradigm shift resonates in cloud-native and AI applications, promoting a developer-centric approach to application development. By moving applications closer to data sources or end-users, the Red Hat edge portfolio enhances application resilience, accelerates speed, and reduces unnecessary resource costs.

The adoption of AI at the edge represents a significant transformation in computing and data processing strategies. By bringing computational power closer to data sources, diverse benefits such as rapid decision-making, reduced network utilization, enhanced reliability, and heightened data security can be realized. With the proliferation of IoT devices and the surge in sensor data, the case for AI at the edge becomes increasingly compelling. In essence, AI is poised to operate in numerous unconventional settings, marking a new era in technological advancement.

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