ABI Research Sees Edge AI SaaS & Turnkey Service Market at $7B By 2025
June 7, 2021
to ABI Research, the global edge Artificial Intelligence (AI)
Software-as-a-Service (SaaS) and turnkey service market will grow at a
Cumulative Average Growth Rate (CAGR) of 46% between 2020 and 2025 to reach
US$7.2 billion in 2025. This is 25% of the global edge AI market, which is
estimated to be US$28 billion by 2025, comprising 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 are low latency and fully secured to assist them with
data-based decision-making. “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,” explains
Lian Jye Su, Principal Analyst at ABI Research.
Sensing this huge market opportunity, public cloud service providers joined the
ecosystem by offering edge AI development boards, hardware systems, software
toolkits, and cloud-based services. Google was the first to offer a development
board with its Edge TPU designed for edge applications. Over the past six
months, AWS and Azure have also strengthened their edge AI portfolio through
hardware and software products, managed services, and industrial 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
“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.
“What enterprises need are industry-grade edge ML models that can be deployed
for various applications across multiple asset categories. Furthermore, not all
enterprises are able to build their own models using tools provided by public
cloud vendors. Building the right edge AI solutions using software from cloud
vendors requires in-depth domain expertise and know-how.”
has led to the emergence of startups specializing in software-as-a-service and
managed services for the design, development, and deployment of edge AI, such as
Edge Impulse, Ekkono Solutions, Imagimob, Mispsology, Qeexo, and SensiML.
"There is huge interest in industrial, asset
tracking and human interface related edge ML applications reinforcing this
market research from ABI," says
Zach Shelby, co-founder and CEO of Edge Impulse,
"In order to make these customers successful we are democratizing ML for their
existing engineering teams, providing end-to-end development optimized for
embedded hardware from Cortex-M0 to Nvidia Jetson already used in over 25,000 ML
algorithms by thousands of enterprises."
"This market study by ABI Research
underscores our long-standing belief that mass adoption of IoT edge applications
will require turnkey AI/ML solutions that automate data science and
significantly reduce the time and expense of firmware development,” said
Chris Rogers, CEO of SensiML. “In fact,
recently we took a large step forward with the launch of our Open Source
Initiative, which aims to accelerate the adoption of TinyML technology by
committing to deliver open and transparent tools to support commercial IoT
products involving AI technology.”
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 skillsets in model compression and hardware
optimization, best practices around data governance, and seamless integration
with other enterprise software and platforms.
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 in a seamless manner. “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.
Imagimob offers an end-to-end SaaS solution
for the development of edge AI applications. We see a lot of interest in the
Anders Hardebring, CEO and co-founder at Imagimob.