We have witnessed the evolution of artificial intelligence (AI). But the search is still on to make more advanced use of AI to improve the customer experience and to streamline their business operations. Due to the overflow of data in organizations from various applications and IoT sensors, almost every organization these days need to make a real-time decision. Thus cloud providers need to incorporate AI into their system.

But the incorporation of AI into the business system is an expensive affair, thus AI applications are now leveraged through providers of as-a-service platforms at a much lower price and lesser risk. Through AI-as-a- service the organization needs to just feed in the data and pay for the algorithms and compute resources. It takes advantage of the already existing infrastructure owned by cloud vendors. AI-as-a-service uses machine learning to optimize data and discover opportunities even for the most difficult situation. Companies need not spend a huge amount of money any more to build infrastructure and technical solutions for storage issues. This has enabled organizations of any size to leverage the benefits of AI by overcoming the investment and expertise barriers. Moreover, this has enabled such organizations to undertake complex AI projects at a fraction of the usual cost, with equal respect to data privacy requirements.

Amazon, Google, IBM, and Microsoft have already developed the platform and have started catering the services. The technology needs custom-engineering according to specific tasks of organizations. AI- as-a-service enables a third-party to offer artificial intelligence outsourcing.

As companies are rapidly moving their business processes over to the cloud, server-less AI applications are in high demand. AI-as-a-service removes the usage of servers and replaces with cloud functions, reducing the operational costs and vendor dependencies.

Kinds of AI-as-a-service that are already disrupting the market are:

Bots and digital assistants: Chat-bots using natural language processing (NLP) algorithms to learn from human conversation and human behavior and to provide answers to queries fall under this category.

This helps the customer service employees to easily finish their task of chatting and focus on more complicated tasks.

Cognitive computing APIs: This enables application programming interface developers to add a specific service or technology into the application they are building without them having to write the code from scratch.

Machine learning frameworks: This helps the developers to build applications that can collect data from the past company record. Moreover, this form of AI-as-a-service provides a way to build in machine learning tasks without needing a big data environment.

Fully-managed machine learning services: This is an add on to machine learning to deal with more complex issues and to create a customized machine learning framework by assisting developers with templates, pre-built models, and drag-and-drop tools.


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