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How Edge Computing and AI Applications Drive IoT

The global pandemic has accelerated the adoption of emerging technologies, including edge computing and TinyML. As more CIOs and CTOs seek ways to capture and process data at the edge of their enterprise IT network, the demand has fueled investment for Internet of Things (IoT) applications.

According to the latest worldwide market study by ABI Research, the global edge Artificial Intelligence (AI), Software-as-a-Service (SaaS), and turnkey service market will grow at a CAGR of 46 percent between 2020 and 2025 to reach $7.2 billion.

This is 25 percent of the global edge AI market, which is estimated to be $28 billion by 2025. The market is comprised of edge AI chipsets, SaaS, and turnkey services, as well as professional services.

As the benefits of edge AI becomes more obvious, enterprises are searching for edge AI solutions that offer low latency and are fully secured to assist them with data-based analysis or decision-making.

Edge Artificial Intelligence Market Development

"The proliferation of edge AI chipset options means enterprises are no longer limited by hardware choices and can select the best-of-breed solution that fits their needs. They now look to invest in SaaS subscriptions, turnkey services, and managed services that can facilitate the deployment of edge AI," said Lian Jye Su, principal analyst at ABI Research.

Public cloud service providers have joined the ecosystem by offering edge AI development boards, hardware systems, software toolkits, and cloud-based services. Google was the first hyperscale provider to offer a development board with its Edge TPU (Tensor Processing Unit) designed for edge applications.

Over the past six months, AWS and Azure have also evolved their edge AI portfolio through hardware and software products, managed services, and industrial sector partnerships. This enables existing cloud service users to get into the edge AI ecosystem, lowering the barrier to entry for enterprises who are not familiar with edge AI development.

"As in many other industries, the entrance of cloud service providers has led to a lot of hype and excitement," says Su. "However, unlike cloud environment that has standardized servers and processors, edge AI is a very diverse market that covers a broad range of device form factor, processing power, and use cases."

According to the ABI assessment, what enterprises need are industry-grade edge machine learning (ML) models that can be deployed for various applications across multiple asset categories. Besides, not all enterprises can build their own models using tools provided by public cloud service providers.

ABI analysts believe that building the right edge AI solutions using software from cloud vendors requires in-depth domain expertise and know-how.

This has led to the emergence of startups specializing in software-as-a-service and managed services to design, develop, and deploy edge AI -- such as Edge Impulse, Ekkono Solutions, Imagimob, Mispsology Qeexo, and SensiML.

These companies tend to provide end-to-end edge ML Operations (MLOps) software and services that enable continuous integration, deployment, and monitoring of edge ML models, often through low code or zero code methods.

In addition, these startups have specialized skill sets in model compression and hardware optimization, best practices around data governance, and seamless integration with other enterprise software and platforms.

Outlook for Edge AI Applications Growth

The future of edge MLOps lies in a higher level of automation through low code or zero code design. This not only lowers the barrier to entry for end-users who do not possess data science or machine learning expertise but also enabling them to perform edge MLOps seamlessly.

"AutoML processes, such as neural architecture search, feature store, hyperparameter tuning, and lifelong learning, allows quick onboarding and development of edge ML models. This allows enterprises to overcome the lack of data science and machine learning expertise and focus on operationalizing edge AI in their assets," concluded Su.

That said, I believe an abundance of data assets will be created, stored, and analyzed outside of the traditional centralized enterprise data center. Moreover, a growing number of IoT applications will not require backhauling data to public cloud service provider facilities. Instead, IT and network colocation providers could experience increased demand for rented space at their shared local facilities.

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