Why Artificial Intelligence as a Service?

In an increasingly data-driven Enterprise context, AI as a Service represents the natural evolution of the Software as a Service (SaaS) approach to Cloud-based technology. Artificial Intelligence and Machine Learning models, thanks to the contribution of Big Data, make it possible to provide on-demand services to automate often repetitive business processes that take time and resources away from more creative tasks.

At the basis of an AIaaS infrastructure, there are cognitive service platforms that animate bots capable of innovating some human-machine interaction mechanisms, such as those based on data entry procedures.

It is clear that an approach to AI as a service would not have been possible without a decisive shift by companies from the on-premises model to SaaS.

For a company, even a highly structured one, the real limitation of a locally implemented infrastructure lies not only in the need to maintain and constantly update its hardware and software but above all in the opportunities to access data. Having access to data, developing algorithms for analysis and processing it, as well as training Machine Learning models, requires a relevant economic effort, time, and resources that cannot easily be estimated.

The relevance of Edge Computing

Edge computing consists of data processing on the spot, where it is collected.

Information is thus analyzed, selected, and optimized at the perimeter: when a sensor collects a piece of data, its processing takes place essentially in real-time through an edge analytics process, a method that maximizes performance in terms of information quality, scalability of computing resources and security.

The decentralization of the infrastructure also guarantees continuity of service and high levels of fault tolerance, making edge computing particularly suitable for business intelligence and Industrial Internet of Things applications.