The Future of Cloud Computing in Edge AI

Cloud computing and edge artificial intelligence (AI) are converging quickly to change the face of IT environments and what businesses can accomplish. 

Everyday consumers interact with edge computing through internet of things (IoT) devices, including wearables and smart home devices, whereas businesses may use edge computing for real-time feedback from sensors on factory floors or municipal infrastructure. 

The future of cloud computing, paired with edge AI, can go in many different directions. We’ll explain what edge AI is, how it can be used in conjunction with cloud computing, and where technology may be headed next.

What Exactly Is Edge Artificial Intelligence?

Edge artificial intelligence (edge AI) involves AI algorithms processing data on-device or in near-edge computing nodes to reduce latency and reliance on centralized cloud resources. A network’s edge is its perimeter, as opposed to data centers that exist at the center.

Edge AI complements AI cloud computing, processing immediate or more time-sensitive data locally while sending insights or aggregated data back to the cloud. 

Some examples of edge AI use cases include: 

• Autonomous vehicles: Self-driving cars need real-time data processing from sensors to make fast decisions about how the car should move or maneuver. 
• Smart homes: Smart thermostats can adjust the temperature based on real-time sensor readings in a house. 
• Industrial IoT: Sensors can also be used in factories to enable real-time process optimizations, like dynamically adjusting factory workflows, and predict when equipment will fail before it breaks down, increasing opportunities for preemptive maintenance.
• Wearable devices: Smartwatches can track health metrics, including fitness and sleep data. Patients can also be given wearables to track potential health issues and give practitioners more information when giving a diagnosis or determining a course of treatment. 
• Retail: Smart shelves can be used to alert workers to inventory stocking needs. They can also provide suggestions for product placement based on in-store traffic patterns. 

What Does Cloud AI Mean?

Cloud AI refers to the AI processing that happens in centralized cloud data centers and uses powerful compute resources, like GPUs, TPUs, and distributed cloud infrastructure. It supports the training and execution of large-scale machine learning models that demand substantial processing power, storage, and integration with other cloud services.

What’s the Difference Between Edge AI and Cloud AI?

There are a few key differences between edge AI and cloud AI. 

With edge AI, algorithms are run on the “edge” of the network, which can include:

• Edge devices
• On-premises edge servers
• IoT gateways
• Multi-Access Edge Computing (MEC) notes
• 5G base stations

Cloud AI processes vast amounts of data across multiple regions and benefits from ongoing updates, real-time collaboration, and access to advanced deep learning models that edge devices may not be capable of handling. Cloud AI typically relies on a stable internet connection and may experience higher latency due to data transmission delays. However, not all cloud AI workloads experience high latency. Some cloud providers optimize these workloads using content delivery networks (CDNs), distributed cloud computing, and AI accelerators like Google’s TPU clusters or NVIDIA GPUs.

Unlike Cloud AI, Edge AI operates locally, allowing it to function even with intermittent or no internet access, as it processes data directly on the device. In contrast, Cloud AI usually requires a stable internet connection for data transmission, real-time inference, and model updates.

Edge AI reduces latency by processing data closer to its source, eliminating delays caused by network congestion or long-distance communication with cloud data centers.

What’s the Role of Cloud Computing in Edge AI?

Cloud computing serves as the foundation that enables Edge AI to operate effectively. While Edge AI processes data locally for real-time decision-making, cloud environments provide the computing power required to train, refine, and update AI models before deploying them to the edge. Cloud AI also enables large-scale data aggregation and analysis, identifying patterns that individual edge devices may not detect. By combining the strengths of both, organizations can create a scalable, efficient AI ecosystem.

How Cloud Computing and Edge AI Work Together

Cloud computing and Edge AI aren’t competing technologies—they complement each other. Cloud computing handles the heavy lifting of model training and refinement, while edge AI executes those models locally for fast, low-latency decision-making. The cloud also acts as a central hub for storing and analyzing data collected from multiple edge devices, ensuring continuous learning and AI improvement.

Common Use Cases for Edge AI

Now that you have a better understanding of how edge AI functions, let’s look at some ways businesses leverage this technology to improve processes and user experiences.

• Model Training and Updates: AI models are typically trained in the cloud, where massive compute resources can process vast datasets. Once trained, these models are deployed to edge devices for real-time inference. Cloud AI also enables continuous model updates, ensuring that edge AI benefits from ongoing improvements.
• Data Aggregation and Analytics: Edge AI pre-processes data locally, reducing the volume of information that needs to be sent to the cloud. This minimizes bandwidth usage and speeds up decision-making. The cloud then aggregates, analyzes, and refines data to detect long-term trends and improve AI models, creating a feedback loop between the edge and the cloud.
• Remote Monitoring: Edge AI can enable remote monitoring of individuals, devices, infrastructure, and equipment. Real-time monitoring can generate alerts for critical events back to the cloud, which can house a centralized dashboard for data and reports from multiple edge locations. 
• Machine Learning (or Federated Learning): AI models are often trained in the cloud and then executed at the edge for real-time decision making (inference). However, federated learning allows edge devices to train AI models collaboratively without sharing raw data, improving privacy and security in industries like healthcare, finance, and telecommunications.
• Backup and Redundancy: Edge devices can generate a lot of data, which can later be backed up and stored in the cloud for long-term storage, compliance, or analysis. Critical edge applications can also gain redundancy from cloud environments, allowing for failover if devices fail. 
• Security and Compliance: Because data can be processed locally, edge AI enhances security and reduces the risk of widespread exposure. 

Benefits of Integrating Cloud Computing with Edge AI

Combining cloud computing with edge AI can lead to a host of efficiencies, enabling businesses to scale intelligently, enhance automation, and drive more informed decision-making.

• Enhanced Computational Power: By integrating edge AI with cloud computing, organizations gain access to high-performance cloud infrastructure for AI model training, deep analytics, and data aggregation. The cloud can process vast amounts of data from multiple edge locations, uncovering trends and refining AI models that are then deployed back to edge devices for real-time decision-making.
• Lower Latency: Edge AI reduces reliance on cloud roundtrips, allowing low-latency, real-time processing of critical data. This is essential for applications like autonomous vehicles, smart surveillance, and predictive maintenance, where even milliseconds of delay could have serious consequences.
• Optimized Bandwidth Usage: Instead of sending raw data to the cloud, edge AI pre-processes, filters, and compresses information, reducing network congestion and optimizing bandwidth usage. Only high-value insights are transmitted, lowering cloud egress costs and improving overall efficiency.
• Greater Cost Efficiency: Integrating edge and cloud AI helps optimize cost allocation by ensuring that workloads are run in the most cost-effective environment. Edge AI significantly reduces cloud data transfer and storage costs by processing data locally, minimizing the need for large-scale data uploads. On the other hand, Cloud AI offers dynamic scalability, which helps reduce infrastructure expenses by adjusting to fluctuating workloads. Hybrid AI models provide businesses with a balanced approach, enabling them to process time-sensitive data at the edge while offloading more complex tasks to the cloud.
• Real-Time Data Processing: While edge AI excels at real-time inference, cloud AI enhances real-time processing through distributed computing, 5G networks, and CDNs. This ensures that AI models remain optimized, synchronized, and responsive to evolving business needs.
• Enhanced Data Privacy: Processing data locally at the edge reduces exposure to cybersecurity risks and ensures regulatory compliance in industries with strict data protection laws (i.e. healthcare, finance, and government). Sensitive information stays within local environments, minimizing unauthorized access and privacy breaches.
• Seamless Scalability: By integrating cloud AI with edge deployments, businesses can dynamically scale computing resources based on demand, network conditions, or device availability. Cloud AI can also centrally orchestrate and optimize multiple edge AI deployments, ensuring consistent performance across a distributed network.

Considerations for Your Cloud and Edge AI Integration

When you integrate cloud with edge AI, you’ll want to keep the following things in mind.

Network Dependence

Edge AI can operate offline or with limited connectivity, but seamless integration with the cloud requires a stable, optimized network. Consider:

• Bandwidth and Latency Requirements: Ensure your network can support timely model updates, data synchronization, and cloud-based analytics.
• Offline Functionality: Plan for graceful degradation when cloud connectivity is disrupted, using store-and-forward techniques, local caching, and scheduled syncs.
• Edge-Orchestrated AI: Leverage cloud-based coordination for workload distribution, even when edge devices operate independently.

Security and Compliance

While edge AI enhances security by keeping data local, information still moves between the edge and the cloud, introducing new risks. To safeguard your integration:

• Implement Zero Trust Security: Every access request must be authenticated, minimizing unauthorized edge-to-cloud data transfers.
• Encrypt Data at Rest and in Transit: Protect sensitive information using AES-256 encryption for storage and TLS 1.3 for transmission.
• Secure AI Models: Prevent adversarial attacks by ensuring model integrity with digital signatures and regular updates.
• Maintain Regulatory Compliance: Different industries face varying cloud-edge data processing regulations (i.e. GDPR, HIPAA, PCI DSS, CCPA).

Data Transfer and Bandwidth Constraints

Cloud-Edge integration requires a strategic approach to data movement to balance cost, performance, and efficiency:

• Intelligent Data Filtering: Instead of transferring raw data, process and filter insights at the edge, sending only the most valuable information to the cloud.
• Compression and Pre-Processing: Use data compression techniques, edge inferencing, and federated learning to reduce bandwidth consumption.
• Hybrid Storage and Selective Sync: Not all data needs to be in the cloud; implement tiered storage models to retain only mission-critical information.

The Future of Cloud Computing in Edge AI

So, what’s next for cloud computing and edge AI? We are likely to see continued advancements in AI model deployments at the edge, hybrid AI systems, and next-generation edge hardware and infrastructure.

• AI Model Deployment at the Edge: We should expect to see continued advancements in how AI models are compressed and optimized to perform more effectively on edge devices that may have limited processing power. More tools and platforms are likely to be developed that automate model deployment more efficiently to larger numbers of edge devices as well. 
• Hybrid AI Models and Systems: Much of this article discusses the collaborative work that can happen between the cloud and the edge, and this collaboration is likely to grow. This can include dynamic task allocation, where systems will assign tasks based on what’s happening in the network, which resources are available, and what latency the systems require to function properly. 
• Edge AI Hardware and Infrastructure Advancements: Specialized hardware that is designed to support AI, such as CPUs and TPUs also work well for edge inference, and we are likely to see more be used in integrative environments. We may also see more small-scale data centers existing closer to the edge, not just devices, that can be managed and orchestrated from the cloud. 

Make Your IT Strategy Your Business’s Competitive Edge

Businesses have a few different options when considering digital transformation projects: jump headfirst into new technologies, hold off until most of their peers have implemented new functionality, or land somewhere in the middle. The ideal approach will depend on the nature of your business, but for most organizations, applying an IT strategy that includes implementing emerging technologies in strategic ways can allow them to gain a competitive advantage. If you’re looking for help with AI or other emerging technologies, learn more about TierPoint’s digital transformation solutions.