Edge platform is a way to optimise operations and drive innovation, for example, in manufacturing, defense, healthcare, retail and logistics domains. Edge computing and IoT transform industries by enabling real-time data processing, autonomous operations, and scalable solutions with secure connectivity and observability through cloud.
Edge platforms aren’t a new thing as such, but due to the rapid leaps in technology lately, they are now mature and cost-efficient to implement. There is a wide range of both software and hardware solutions available for easy to set up the stack, including data ingestion from various sources and running AI / ML applications on the edge with a low-code type of development interface. Gartner predicts that by 2026, at least 50% of edge computing deployments will involve machine learning, compared to 5% in 2022.
At the end of 2024, about 20 % of enterprises have deployed edge computing, with a plan to double that within two years (Gartner). A Platform approach, e.g. one that fosters interactions and transactions across diverse systems and users, is required, as the use cases grow and evolve, and become also more business critical. There are three main drivers for this highly trending edge platforms: the need to modernise scattered legacy factory floor databases and get more value out of that data, business servitisation, and the aim to agile and cost-efficient innovation e.g. with digital twins. Enterprises now have technological capabilities like a mature enterprise data/data science platform and increased cloud adoption, which enable modern and efficient end-to-end solutions to become truly data-driven.
Edge-first thinking drives for optimal results and long-term value
“Cloud first” has been the name of the game for some time. Now the edge technology has matured, cloud costs are increasing, and some use cases require instant response and resilience. That is why the perspective is changing into “edge-first”, and the key is to optimise workloads, data flows and utilisation of AI/ML between edge and cloud. Edge use cases vary depending on the industry domain, for example:
- Manufacturing: Collecting IoT data to optimise operations excellence topics like predictive maintenance, process visualisation, anomaly detection, industrial robots and autonomous vehicles.
- Retail: Hyper personalised offers in the store and shelf or storage monitoring with machine vision. Gartner predicts that by 2027, two-thirds of all tier 1 multichannel retailers will have edge computing deployed in their stores.
- Defense: Management of unmanned and autonomous systems, predictive maintenance of military vehicles, ships, and aircraft, edge devices on drones or ground units to analyse sensor and video feeds in real-time or wearable devices.
- Healthcare: AI-powered wearable devices for health monitoring, patient vitals analysis from multiple data streams to detect early signs of condition, AI-driven tools in the operating room for tasks like robotic precision.
Even though the use cases might differ from domain to domain, there are certain factors that are common to all: Typically, there is a need for low latency and real-time data loads and support for instant decision making without relying on distant data centers only.
Secondly, edge plays a role in environments with intermittent or limited connectivity. Thirdly, scenarios where information and privacy must be secured, edge can handle it without sending data further. Efficient and secure reusability and scalability of applications and ML models in remote sites is also a common factor for edge platforms.
As a summary, edge computing has a transformative impact on businesses through its ability to process data closer to the source of generation, allowing several strategic benefits. Not only does edge computing minimise latency, but it also reduces bandwidth costs by minimising the amount of data transmitted to centralised clouds for processing, while facilitating immediate insights and actions based on local data analysis, keeping private information locally and secure. Decentralised processing ensures that edge devices can operate independently, even during network outages.
Edge computing unlocks opportunities for innovation in services and applications that require near-instant processing, for example, with digital twins and metaverse applications. It supports the rapid scaling of IoT networks and AI-driven applications by reducing the dependency on central systems. Edge technology also enables modernising devices by turning non-smart devices into intelligent ones. Attention should be paid to the maintenance and lifecycle-related costs and capabilities, and security of the edge devices.
“Edge-first” remains as a perspective, but different strategies and approaches depend on the industry and use case circumstances:
- Cloud-out, where cloud is the primary and central environment, and edge is a lightweight extension to that, e.g in retail use cases.
- Edge-in, meaning that edge is the primary environment, and cloud services are pulled in as per need, like in defense or remote site scenarios.
- Usually a combination of both worlds.
How to get started with the edge journey?
There are two angles on how to get started. In the pre-study phase, the key is to identify, map and evaluate the business use cases and opportunities, and what value those could generate either from an operations excellence point of view or revenue stream and customer value point of view. The other angle is to assess the current state of capabilities, like technology, data assets, sensors, competence, and existing bottlenecks and problems.
Based on the previous findings, the design of the platform can be started: evaluation of the applicable technological options, both software and hardware, and creation of a reference architecture, needed instrumentation and data collection points. All decisions and selections at this phase is good to document with reasoning for future reference. The first potential show use case and pilot business area should also be selected for the MVP phase. Never underestimate the power of concrete and impactful demo use case to drive change and keep management committed to the investment ahead!
Now the minimum viable edge platform can be developed. The selected use case validates that the designed and developed platform works as it should. When more use cases get onboarded and scaled out, it might be a good idea to return to the design table and review if the tech components are still valid or should they be switched, e.g. from open source to commercial solutions. Of course, the whole lifecycle of the platform and applications running on that should be taken into account from the start. Meaning building observability, accessibility, needed environments, devops pipelines and automation and support services, to name a few. Show the value, scale the platform and use cases!
We have all the capabilities needed to create impact and long-term business value by combining design, development, data, AI/ML, connectivity, cloud/edge and information security.
Contact us to discuss your use case scenarios and architectural options, and learn more:
- See our Crash course recording about Edge platforms and NVIDIA demo
- Our customer examples about Ponsse and Fingrid