Moving AI to the edge: Benefits, challenges and solutions

Artificial intelligence (AI) has transformed dramatically since its inception in the 1950s, particularly in the last decade when there have been unprecedented advancements in the field.

While early AI implementations focused on controlled environments and specific tasks, today's AI systems are being deployed in increasingly diverse scenarios. Autonomous vehicles process sensor data in real time to navigate complex environments. Smart manufacturing systems detect quality issues on production lines. Healthcare devices monitor patients’ vital signs and detect anomalies. Smart cities utilize AI for everything from traffic management to public safety.

However, these emerging use cases often present unique challenges that traditional, cloud-based AI architectures struggle to address. Consider an autonomous robot in a manufacturing facility that needs to detect and respond to potential safety hazards in milliseconds. The latency involved in sending data to a cloud server for processing and waiting for a response could be dangerous. Similarly, a smart medical device processing sensitive patient data may face both privacy regulations and connectivity constraints that make cloud processing impractical.

Coinciding with AI's evolution, we have seen the rise of edge computing, a paradigm that brings computation and data storage closer to the location where it's needed. This approach offers compelling benefits, but can these advantages be effectively applied to AI workloads? What are the tradeoffs involved in moving AI processing from powerful cloud datacenters to more constrained edge devices?

In this article, we'll explore the intersection of AI and edge computing, examining how this combination can address emerging challenges in AI deployment. We'll analyze the benefits this approach offers, the technical challenges it presents and the architectural considerations necessary for successful implementation. Through this exploration, we'll provide a framework for understanding when and how to take advantage of edge computing for AI applications in the real world.

Where AI meets the edge

The AI lifecycle involves multiple stages: data collection and preparation, model development, training, validation, testing, deployment, inferencing, monitoring and retraining, all managed through a combination of machine learning (ML) system development and operations (MLOps) to streamline the process. While most of these steps require significant computational resources and are typically performed in core datacenters or cloud environments, it's the inference (where the trained model processes inputs) that is increasingly being moved to the edge.

Consider a manufacturing company using computer vision for quality control: the complex process of training the model with thousands of images of defective and non-defective products happens in the datacenter, but the actual real-time analysis of products on the production line (inference) occurs on edge devices right on the factory floor. Similarly, in a smart retail environment, while customer behavior models are trained centrally using aggregated data from multiple stores, the real-time customer interaction predictions happen locally in each store's edge devices.

While inference is the most common edge AI workload, some organizations are beginning to perform limited training or model fine-tuning at the edge. For instance, autonomous robots might adjust their AI models based on local conditions, or a smart building might fine-tune its energy optimization models based on specific usage patterns. Additionally, federated learning, where edge devices contribute to model training without sharing raw data, is emerging as a promising approach for confidential, sensitive applications like healthcare, where hospitals can improve AI models while keeping patient data local.

Transfer learning at the edge is another emerging use case, where pre-trained models are slightly modified using local data to better adapt to specific conditions. For example, a generic machine failure prediction model might be fine-tuned at each factory location to account for specific equipment configurations and environmental conditions.

In summary, AI inference remains the primary use case at the edge, while other stages of the MLOps lifecycle are gradually gaining traction. Another key consideration in the convergence of AI and edge is determining whether edge architectures are best suited for generative AI (gen AI) or predictive AI applications.

While edge computing has traditionally excelled in predictive AI applications—for example, equipment failure prediction in oil and gas facilities, crop health monitoring in precision agriculture or early warning systems for natural disasters—its benefits are increasingly relevant to specific gen AI use cases, as well. Consider scenarios like real-time language translation in remote areas, personalized AI avatars running on consoles or point-of-sales or AI-powered tools for people working on site with limited connectivity. Even text-to-speech applications for accessibility devices can benefit from edge deployment, providing consistent performance regardless of network conditions and maintaining user privacy. Though gen AI typically requires more computational resources, careful model optimization and the increasing power of edge devices are making these applications more feasible, particularly in scenarios where latency, privacy or reliability are vital.

Benefits of deploying AI at the edge

When designing AI solutions, the choice of where to run each step significantly impacts the system's overall effectiveness. Edge computing, which brings AI processing closer to input sources, offers compelling advantages that make it increasingly attractive for modern AI deployments in certain use cases.

Let's explore three key benefit areas that are driving this architectural shift.

1. Performance and reliability

In the realm of performance, edge computing dramatically reduces latency, a critical factor for many AI applications. Consider an autonomous vehicle or robot that needs to make subsecond decisions: processing sensor data locally can reduce response times from hundreds of seconds to milliseconds, potentially making the difference between avoiding or experiencing a collision. This same principle applies to industrial settings, where AI-powered quality control systems can inspect products on high-speed production lines without the delay of cloud communication.

Edge computing also supports system reliability in challenging conditions. Mining operations, for example, can maintain AI-assisted safety monitoring even in underground environments where network connectivity is unreliable. Similarly, emergency response systems equipped with edge AI can continue functioning during natural disasters when cloud access may be compromised. This independence from network connectivity proves invaluable in remote locations or during critical operations where system downtime isn't acceptable.

2. Security and compliance

The security benefits of edge AI architectures are particularly relevant. In healthcare settings, edge processing allows medical devices to analyze patient data locally, simplifying compliance without transmitting sensitive information to the cloud. Smart home systems can perform facial recognition and voice processing on-device, protecting a resident’s privacy while maintaining functionality.

By keeping data processing local, financial institutions can analyze transaction patterns for fraud detection without exposing sensitive data to network vulnerabilities. This distributed approach eliminates single points of failure and simplifies compliance with data residency requirements, particularly important for organizations operating across multiple jurisdictions.

3. Cost and efficiency

The economic advantages of edge AI extend beyond direct operational costs. Consider a network of smart surveillance cameras: processing video feeds locally means only relevant events are transmitted to the cloud, significantly reducing bandwidth costs and storage requirements. This approach is particularly impactful in internet of things (IoT) deployments, where thousands of sensors might otherwise be continuously streaming raw data to central servers.

Energy efficiency represents another significant cost benefit. By optimizing processing for specific hardware and reducing network communication, edge AI systems can operate more efficiently than their cloud dependent counterparts. A smart manufacturing facility using edge AI for quality control not only saves on cloud computing costs but also reduces its carbon footprint through optimized local processing and minimal data transfer. These efficiency gains become increasingly important as organizations focus on both environmental impact and operational expenses.

The combination of these benefits makes edge computing a compelling choice for many AI applications, particularly those requiring real-time processing, enhanced privacy or operation in challenging environments. As we continue to push the boundaries of AI capabilities, the ability to process data at the edge becomes not just an alternative, but often a necessity for modern AI architectures.

Key challenges of moving AI to the edge

The deployment of AI workloads at the edge presents distinct challenges that organizations must carefully consider in their architectural decisions. Understanding these challenges across three key dimensions helps in developing effective strategies to address them.

1. Technical limitations and constraints

The physical constraints of edge computing create unique challenges for AI deployment. Consider a smart agriculture system using computer vision to monitor livestock health: the edge devices must process complex neural networks with limited processing power and memory. Similarly, smart city traffic management systems must handle multiple AI models for vehicle detection, emergency response and pedestrian safety, all while operating within the confined resources of devices running on the streets. These technical limitations often require significant model optimization as they try to balance between maintaining AI accuracy and meeting hardware constraints.

The complexity extends to environmental challenges, as well. Edge AI systems in construction sites must operate reliably despite dust, vibration and temperature variations, while maritime applications need to process data in corrosive, high-humidity environments. These conditions demand robust hardware solutions and careful thermal management strategies, particularly when running computation-intensive AI workloads.

2. Operational and management challenges

Managing a distributed network of AI-enabled edge devices presents significant operational complexities. For example, take a retail chain deploying AI-powered inventory management systems across thousands of stores ensuring consistent model performance, managing updates and monitoring system health across all locations or a smart building system using AI for climate control and security that must maintain continuous operation while handling regular software updates and model refinements without disrupting essential services.

Quality assurance presents another critical challenge. For example, transportation systems using AI for railway crossing safety need rigorous testing across various scenarios. Similarly, AI-enabled medical imaging devices in remote clinics require consistent performance validation while managing limited IT resources and varying environmental conditions.

3. Implementation and integration challenges

The diverse landscape of edge hardware and existing systems creates significant integration hurdles. Consider a pharmaceutical company implementing AI for production line quality control. The solution must integrate with legacy manufacturing equipment while meeting strict industry regulations. Or consider how smart retail systems need to interface with existing inventory management solutions, point-of-sale systems and security infrastructure, all while maintaining real-time AI processing capabilities.

Security implementation becomes particularly challenging in distributed environments. AI-enabled banking ATMs must protect against both physical tampering and cyber threats while maintaining customer privacy and regulatory compliance. Similarly, smart grid systems using AI for power management must provide security-focused operations across widely distributed infrastructure while preventing unauthorized access to critical controls.

AI at the edge with Red Hat

Red Hat's extensive portfolio provides a robust foundation for addressing edge AI challenges through flexible platforms, automated management tools and security-hardened integration capabilities. Let's explore how these solutions effectively address the key challenges organizations face when implementing AI at the edge.

Overcoming technical limitations and constraints

Red Hat's hardware and software certification program lets organizations confidently deploy edge AI solutions across diverse environments. Through extensive partnerships with hardware vendors, Red Hat provides freedom of choice in building edge AI infrastructure that meets specific requirements, supporting both x86 and ARM systems (under the SystemReady compliance program).

This flexibility extends to the platforms. For more powerful edge locations, Single Node OpenShift or Three-Node Compact OpenShift Cluster provides a complete Kubernetes platform with advanced capabilities. Its operator lifecycle manager simplifies platform upgrades and application management, making it easier to maintain AI workloads at the edge.

For more constrained environments, Red Hat Device Edge combines Red Hat Enterprise Linux (RHEL) with Red Hat’s build of MicroShift (a lightweight Kubernetes distribution) to enable containerized AI workloads on resource-limited hardware. Red Hat Device Edge introduces efficient image-based updates through OSTree, contrasting with traditional package-based updates in RHEL. OSTree enables efficient image-based operating system updates through a "git-like" approach that only transfers the changed portions of the system, significantly reducing bandwidth usage and update times compared to traditional package-based updates. This atomic update system enables system reliability with automatic rollback capabilities if an update fails, making it ideal for managing remote edge devices with limited connectivity.

Looking ahead, RHEL will soon introduce general availability (GA) bootc support. Bootc represents a paradigm shift in operating system management by implementing bootable containers, where the entire OS is treated as an immutable container image. This approach provides atomic updates with built-in rollback capabilities, reduces system overhead and enables consistent deployment patterns across edge devices. 

Addressing operational and management challenges

Red Hat's solutions streamline operational complexity through automated pipelines and comprehensive management tools. Red Hat Advanced Cluster Management for Kubernetes enables fleet-wide policy enforcement and consistent configuration across distributed edge locations. For example, a smart city deployment managing thousands of traffic cameras with AI-powered incident detection can use Red Hat Advanced Cluster Management to provide consistent security policies and configurations across all edge locations. 

The image-based approach in Red Hat Device Edge provides reliable rollback capability and significantly simplifies fleet management, supports system integrity and reduces the risk of failed updates, crucial benefits for managing large-scale edge AI deployments where system reliability is key. 

At the same time, OpenShift allows unified management of both Kubernetes and the underlying operating system from a single interface, taking advantage of cluster operators to automate lifecycle management tasks, such as updates, configuration enforcement and system health monitoring, providing consistency and reliability across the entire deployment.

Red Hat Ansible Automation Platform complements these capabilities by enabling automated configuration management across the fleet, providing consistency, reducing manual intervention and accelerating deployment processes. With Ansible’s event-driven automation, organizations can enforce security policies, apply system updates and maintain compliance at scale with minimal effort.

On top of that, Red Hat is actively contributing to the upstream project Flight Control that provides pull-mode configuration management, a simplified UI, an intuitive API for seamless integration and robust features like device inventory tracking and policy enforcement. Flight Control enables users to declare the desired state for the operating system version, host configuration and application set for individual devices or entire fleets, with an intelligent agent providing rollout, enforcement, and real time health reporting. It integrates with bootc image-based Red Hat Device Edge systems and supports containerized workloads on Podman or Kubernetes along with traditional applications or virtual machines (VMs), making large-scale edge architectures not only efficient and resilient but also flexible.

Additionally, Red Hat's integration with OpenTelemetry provides comprehensive observability for edge AI workloads, enabling organizations to collect, process and analyze telemetry data from distributed models and applications. Through the combination of Red Hat OpenShift distributed tracing, Prometheus for metrics and OpenTelemetry collectors, teams can monitor workloads and model performance, track inference latency and diagnose issues across their edge AI deployments while maintaining a centralized view of their distributed infrastructure.

Regarding the MLOps workflow, Red Hat OpenShift AI supports multiple AI inference runtimes and provides MLOps capabilities through tools like Kubeflow and OpenShift Tekton pipelines, enabling organizations to automate model deployment, testing, and updates for both predictive and generative AI use cases. The platform offers KServe Raw Deployment option for direct inferencing without framework dependencies, significantly reducing the infrastructure footprint by eliminating serverless requirements.

This lightweight approach makes it ideal for edge computing scenarios, particularly when using MicroShift for AI model deployment at the edge. Models can be versioned and stored in the OpenShift AI Model Registry, which along with S3 storage, can take advantage of the Open Container Initiative (OCI) specifications to manage models as container images in standard container registries. This container-based distribution approach, combined with KServe's ModelCar capability, which enables serving models directly from container images, creates a streamlined model distribution and inference across edge locations.

Resolving implementation and integration challenges

The open source nature of Red Hat's portfolio, combined with its extensive partner ecosystem, enables organizations to adapt their edge AI infrastructure as requirements evolve, while maintaining enterprise-grade protection and reliability throughout their deployment. Red Hat's open source foundation and commitment to flexibility simplify edge AI integration into existing environments. For example, OpenShift AI allows the usage of multiple AI/ML frameworks, enabling organizations to choose the best tools for their specific needs. 

For critical infrastructure, such as power plants using AI for equipment monitoring, Red Hat's security-focused approach provides multiple layers of protection. System-wide encryption, security compliance tools for both Red Hat Device Edge and OpenShift, and automated security updates help maintain a robust security posture across the entire edge AI infrastructure. The comprehensive security features in both OpenShift and RHEL protect sensitive business data, while the compliance tools help maintain regulatory requirements across all locations.

Lastly, through their real-time kernel support, both OpenShift and Red Hat Device Edge deliver deterministic processing capabilities with predictable latency, enabling AI workloads that require precise timing and consistent response times, essential for applications where even milliseconds of delay could impact system effectiveness. For example, PLCs (programmable logic controllers) take advantage of real-time AI models to make microsecond adjustments, optimizing manufacturing processes and reducing defects. Similarly, AI-driven tracking with deterministic latency allows drones in defense applications to perform precise target recognition and real-time navigation.

Final thoughts

The shift towards edge computing in AI architectures represents a strategic evolution in how organizations deploy and manage their AI workloads. Whether processing safety equipment detection in factory environments or handling sensitive patient data analysis in remote healthcare facilities, edge AI architectures deliver crucial benefits, such as reduced latency, enhanced data privacy and security capabilities through local processing and optimized operational costs through reduced bandwidth usage and efficient resource utilization.

Red Hat's comprehensive portfolio directly addresses the challenges of edge AI deployment while preserving these advantages. Through solutions like OpenShift and Red Hat Device Edge with MicroShift for resource constrained environments, organizations can deploy AI solutions that match their specific requirements. The automated management capabilities provided by Red Hat Advanced Cluster Management and Ansible Automation Platform enable consistent operations across distributed locations, while OpenShift AI's MLOps tooling streamlines model deployment and updates. This integrated approach, backed by Red Hat's extensive partner ecosystem and security-focused foundation, lets organizations confidently implement edge AI solutions that meet both their current needs and future scalability requirements.