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  • Writer's pictureTeam ElkanIO

2 Types of AI as a Service (AIaas) companies.

In this era of Artificial intelligence, we have been posed with the dilemmas of choosing the right company to deliver a solution. As this is an upcoming area, many of the already existing as well as young start-ups are trying to get their hands on the same.

There are broadly two types of firms which are trying to contribute their fair share into the AI sphere. Nowadays, it is possible because of the available cloud solutions in the market which support the services to enable business transformation initiatives.

How things are for traditional IT service companies...

One of them is a traditional software service company focused on web-mobile solution development. They need to follow, 'ride the wave technique' to get sustainable in the market. Also, they would like to taste a piece of AI cake.

Common problem witnessed with the traditionalist approach is that they will try to solve the problem by plugging in different available solutions and cloud services. Most of the time, it may end up in compromising the scope or user requirements. Finally, it won't make a perfect fit solution.

For AI as a Service (AIaaS) companies...

Another set of companies are focused on building custom solutions to solve the problems. Their primary objective is to become an AIaaS- AI as a Service company. Instead of available cloud solutions/services out there in the market, they develop custom algorithms and frameworks to solve customer needs.

The cynosure of a custom solution is extensive research and gaining knowledge of relevant algorithms. One should know, which approach or an algorithm will be a perfect fit for a particular business issue. They need to architect one solution accordingly.

Cost-effectiveness and security concerns

A custom approach is cost-effective and we have an option to tune the output quality. It is possible by tweaking training approaches, quality and quantity of data set etc. The on-premise deployment will make the system more secure by avoiding data sensitiveness.

Along with the way the industry is getting matured, more solution development infrastructure may come into play. Still, industry experts are trying to figure out AI touch points in their business processes. Many POCs and pilot runs are going on, in industry majors. We can expect an exponential change in a production level deployment in the coming months.

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