Additionally, the burgeoning requirement for greater efficiency in data processing is further boosting the need for edge AI software. Besides the aforementioned factor, the surging adoption of cloud computing solutions by enterprises is also fueling the demand for this software around the world. AI-integrated applications that are deployed on the cloud usually have latency issues and provide slow responses.
As the edge AI software can make the applications function with high bandwidth and low latency by keeping the computer resources at the edge of the network, it is being increasingly adopted by businesses. The other major factor pushing up the requirement for the edge AI software is the rapid advancements being made in the internet of things (IoT) applications.
IoT applications deal with various devices that interact with each other over the internet, with the AI assisting them in learning from the data collected. Moreover, IoT enables the trading of data between different machine parts or production lines over a wireless network. Due to these factors, many organizations are actively focusing on developing enhanced IoT-based applications and gadgets, thereby propelling the growth of the edge AI software market.
This market research report provides a comprehensive overview of the market
- Future potential of the market through its forecast for the period 2020– 2030
- Major factors driving the market and their impact during the short, medium, and long terms
- Market restraints and their impact during the short, medium, and long terms
- Recent trends and evolving opportunities for the market participants
- Historical and the present size of the market segments and understand their comparative future potential
- Potential of on-demand logistics services, so the market players make informed decisions on the sales of their offerings
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