IoT, AI and ML services to Grow by US$3.6billion Till 2026

ABI Research predicts the growth of IoT, AI, and ML

Research by ABI showed that artificial intelligence and machine learning are expected to grow within the Internet of things domain by US$3.6 billion by the end of 2026. Since IoT analytics are about to converge with the big data domain, the value in the technology is predicted to shift beyond hardware to analytics adding value to the services. The report states that Covid-19 didn't have any negative impacts on the IoT data analytics but has been advantageous to the emerging cloud-native data-enabled analytics.

Since most of the industries are moving towards remote services, solutions that are unique for remote monitoring, asset management, asset visibility, and predictive maintenance are booming in the market. When coming to the vendors such as DataRobot, they are moving towards ML and AL tools to deploy edge, cloud using platforms such as a Service (PaaS), and Software as a service (SaaS).

"All and all the Covid-19 pandemic highlighted the importance of rapid-deployment solutions, such as hardware agnostic SaaS", said Kateryna Dubrova, research analyst at ABI Research.

Major tech companies such as AWS, C3, and Google have also been victorious in promoting their analytics toolsets and products with the help of centralized repositories for Covid-19 data. These data are currently public and are not being monetized. But it can be helpful to use data lakes to create products for sale to the healthcare market shortly. When thinking about the technology, these data lakes could be the first step for testing data visibility services.

Big data and analytics may not be a solution for the virus but the interesting part is that IoT-data-enabled technologies have been proved in claiming the public and to monitor patients for other new breakouts.

Dubrova concludes by saying that "AI and ML usage has fastened during the pandemic period. The AI and Ml in the IoT are at the stage of development and lack of evolving in data-enabled infrastructure has limited rapid adoption of machine learning on an operational level when Covid-19 accelerated".