Edge solutions for real-time decision making

Edge devices are nodes on the network that perform computing at or near the physical location of a user or data source. They exist across almost every industry, from the point-of-sale (POS) machine in your grocery store to the robotic arm building your car.

But organizations face a key challenge at the edge: They often track large amounts of data and have to send it back to a core cloud for processing before it returns to the device for action. This journey adds time and complexity to the process, slowing operations and resulting in older, less relevant data.

More and more organizations:

• Want to implement new use cases, which require latency-sensitive applications.
• Must process large volumes of data, which are slow to send to a public cloud.
• Need the ability to make quick decisions to respond to evolving competition and customer expectations.

In the face of these new challenges, organizations need solutions for using real-time data analysis and artificial intelligence (AI) to make intelligent decisions faster at the edge.

Let's explore some industry-specific examples where real-time data processing and intelligent decision making can make a big difference.

Manufacturing 
In manufacturing environments, integrating AI with visual inspection systems can improve quality control and worker safety. AI models can detect safety hazards or defects in products on an assembly line in real time. This can make inspections more accurate and catch problems earlier in the manufacturing process. However, it requires data to be processed by the AI model and then acted upon by the device very quickly.

Using devices to identify and understand real-world objects in this way is known as computer vision. Businesses that employ computer vision need an edge solution that works across multiple devices with varied computing power, all functioning as a unified platform.

Why the future of manufacturing will rely on open source 

Retail 
In the retail sector, edge computing and AI can enhance customer experiences and operational efficiency. For example, AI-powered systems can manage inventory by automatically monitoring stock levels and  initiating restocking as needed. Edge devices can also process customer data locally to offer personalized shopping experiences and promotions directly at the point of sale―all in real time.

To implement systems like these, an organization must manage the data from edge devices across a unified edge solution that has access to AI hardware acceleration. Most importantly, the systems must integrate to avoid sending inaccurate data, such as offers about out-of-stock inventory.

Healthcare 
Edge computing is transforming healthcare delivery with real-time analysis of medical diagnostics at the point of care. For example, edge devices equipped with AI capabilities can help interpret ultrasound images on site. In locations where trained technicians may be scarce, this capability can expedite medical decisions, potentially saving lives. A healthcare application like this requires an edge solution that safeguards private data, enforces security standards, and protects the software supply chain.

While these use cases have the potential to transform businesses―and even save lives―organizations face hurdles in implementing them. Here are some specific challenges and how to overcome them.

Handling massive data volumes locally
Most edge devices and sensors generate a lot of data. Traditional data-processing methods that send data back to a central datacenter may not be viable due to latency and bandwidth constraints. Processing and analyzing data at the edge removes the requirement to transmit large amounts of data, decreasing latency and increasing efficiency.

Processing more complex tasks
Making intelligent decisions at the edge often requires applications to process data on the edge hardware itself. So these devices need extra processing power―or to more efficiently use the processing power they have.

Also, when you embed AI in edge devices, you need an application platform that scales with those devices.

Ensuring privacy and security
More data processing at the edge introduces new vulnerabilities to the supply chain. For example, storing sensitive health data on an edge device creates a vulnerable attack surface. To protect privacy and sensitive data, you’ll need a platform that can help operationalize security protocols as a part of your DevOps and AI process.

We recognize that every organization has its own edge requirements, and no single solution can address them all. A unified platform that offers a broad range of features, capabilities, and partner integrations can extend your open hybrid cloud environment across edge, core, and cloud locations―so you can develop and run any workload, anywhere.

Incorporating AI with this integrated approach provides flexibility across the hybrid cloud. It also lets you add your customer data to pretrained or curated foundation models, giving your organization the freedom to use a variety of hardware and software accelerators. Red Hat’s approach to AI at the edge can help you:

Add flexibility and consistency with an application platform

Our solutions start with Red Hat® OpenShift®, a single application platform that extends Kubernetes capabilities from core to cloud to edge computing environments. For edge-specific uses, it offers options ranging from multinode, high-availability clusters to small-form-factor, single-node topologies. These options help your organization make rapid decisions at the edge by providing flexible and scalable deployment options tailored for your edge environments.

In addition to flexibility, Red Hat OpenShift brings much-needed consistency by allowing developers to write an application once for deployment anywhere, and allowing operations teams to manage a single environment―from core to cloud to edge. It also lets developers use tools and processes they’re already comfortable with, easing the burden of configuring, deploying, provisioning, managing, and monitoring even the largest-scale containerized environments. With Red Hat OpenShift, you get this consistent experience wherever your applications need to be—on premise, in a public cloud, in a factory or hospital, or even in orbit.

Build for AI

Additionally, Red Hat OpenShift is scalable and suitable for AI workloads, complete with access to popular hardware accelerators. And Red Hat OpenShift AI provides operationally consistent capabilities that let your teams experiment, serve models, and deliver innovative applications. This can help personalize customer experiences and support better visibility and management of assets on the factory floor, or in the field. It’s especially important if you’re powering AI workloads, like those used in computer vision.

Design for the edge

Devices at the far edge are limited to a small footprint and few resources. Using a platform designed to work in lightweight configurations is important because you can deploy it on edge devices that have been custom-designed for specific tasks. And you can manage it all with 1 set of tools and processes that your teams are familiar with. Red Hat Device Edge offers lightweight deployment options with optional container orchestration. This allows for local data processing and AI and ML workload execution, minimizing latency and supporting real-time decision making.

Start with a reliable foundation

Red Hat Enterprise Linux®, Red Hat’s core operating system, offers a stable, security-focused foundation for running AI workloads at scale. It supports latency-sensitive applications so AI models can operate efficiently on a variety of hardware, from edge devices to central datacenters.

This flexibility lets you deploy AI and ML applications across your entire infrastructure with consistent performance and reliability. As a result, you can use data intelligence more efficiently to optimize operations, enhance customer experiences, and offer better products and services to your customers.

Extend the advantages of automation to the edge

Scaling operations to connect to the edge can be challenging due to the hundreds of thousands of steps and configurations required to do so. Red Hat Ansible® Automation Platform reliably scales capacity for local and remote automation workloads. With built-in health checks to determine the optimal nodes to run automation jobs on, Ansible Automation Platform is the glue that holds your IT infrastructure in place.

Bring the power of global partnerships and AI to the edge

Red Hat’s hardware partners―such as NVIDIA, Intel, and AMD―offer solutions that handle hardware-intensive AI workloads at the edge and integrate with our open source products.