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IoT Machine Learning and AI Services Gain Momentum

As more devices are connected to the public internet, mass quantities of data are being produced. Capitalizing on those data assets, expansion in the advanced analytics market has been enabled by new technology, products, and related services. 

The inherent value of data is increasing, and that value is stimulating the Internet of Things (IoT) advanced analytics market, with the emergence of accessible out-of-the-box and off-the-shelf machine learning (ML) and artificial intelligence (AI) solutions.

Vendors are now easing access to ML and AI toolsets by expanding availability through deployment options that include edge computing, on-premises infrastructure, private cloud, Platform-as-a-Service (PaaS), and Software-as-a-Service (SaaS).

 IoT ML and AI Market Development

According to the latest worldwide market study by ABI Research, the IoT ML and AI market will reach $1.09 billion in 2020 and grow to reach $10.6 billion by 2026.

Edge ML/AI is more prevalent in manufacturing and industrial segments, where there is an immediate need to assess, transform and augment data as it is being generated through functions of quick pattern recognition, labeling, and protocol optimization.

"The IoT Edge Advanced Analytics Market is essentially operationalized ML and AI products and services targeted at Operational Technology (OT) teams to understand and extract insights," said Kateryna Dubrova, research analyst at ABI Research.

According to the ABI assessment, ML and AI frameworks are also enabling advanced analytics in the public cloud, where algorithmic models (predictive, prescriptive, correlations, etc.) are deployed on pre-processed and organized datasets.

Amazon Web Services (AWS), Microsoft Azure, Google Cloud, SAS, and C3.ai, are dominating the scene for their end-to-end IoT portfolios and combined native and third-party ML/AI toolkits -- all predominantly delivered as public cloud offerings.

At the same time, Seeq, DataRobot, Noodle.ai, and Dataiku will soon enable greater democratization of IoT ML technologies, with more powerful AI engines and low-code or no-code software solutions.

Finally, there is steady and robust development among edge-centric SaaS and PaaS vendors -- such as Crosser, Swim.ai, and FogHorn, advocating edge-first solutions.

While the vendors have clear positions on deployment choice, edge and cloud are merging into a singular edge-cloud paradigm. However, the increasing value of edge AI/ML solutions within the IoT has unveiled a gap in the accessibility of these solutions.

ABI research concludes that the scalability and productization of an edge solution are fundamentally dependent on cloud vendors expanding their marketplace portfolios toward the edge. The IoT edge marketplace will take off within a couple of years and become an integral part of the IoT ecosystem. 

Outlook for IoT Intelligence Applications Growth

But not all suppliers in the IoT value chain will find greater accessibility to off-the-shelf AI/ML solutions favorable to their business model. These solutions will lessen the need for and duration of analytics professional services.

"Fortunately, IoT is a growing market so custom analytics engagements will still see demand. The real upside is that more people can apply advanced analytics to their IoT data expanding its usefulness to a broader cross-section of the enterprise," Dubrova concludes.

I believe that IoT big data, and associated analytics software applications, will continue to blossom across the globe as more organizations seek to extract actionable insights from raw data repositories. 

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