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Artificial Intelligence as a Service (AIaaS) is the third party offering of artificial intelligence (AI) outsourcing. AI as a service allows individuals and companies to experiment with AI for various purposes without large initial investment and with lower risk.
We’ve all heard of IaaS and SaaS before. These terms have become ubiquitous for infrastructure-as-a-service and software-as-a-service. Another variant is PaaS, short for platform-as-a-Service.
Today, most companies are using at least one type of “as a service” offering as a way to focus on their core business and spend less money on an important service.
Now the same methods that made other “as a service” offerings so popular are being applied to a new field: artificial intelligence. AIaaS is short for AI-as-a-service. The term and the product are on the rise, and we’re digging into what AIaaS means in this article.
The concept of “everything as a service” refers to any software that can be called upon across a network because it uses cloud computing. In most cases, the software is available off the shelf, meaning that you can buy it from a third-party vendor, make a few tweaks, and begin using it nearly immediately, even if it hasn’t been totally customized to your system.
For companies that can’t or are unwilling to build their own clouds and build, test, and utilize their own artificial intelligence systems, AI-as-a-service is the solution. Like other “as a service” options, the same approach is applied to artificial intelligence:
- Allowing the company to focus on their core business, not becoming data and machine learning experts
- Keeping costs transparent
- Significantly lowering risk of investment
- Increasing the benefits from data
- Increasing strategic flexibility, because AI-as-a-service is flexible and dynamic
Types of AIaaS
If we understand AIaaS as artificial intelligence off the shelf, what are we getting when we purchase a service?
Well-known types of AIaaS include:
- Bots and digital assistance. These can include, for example, chat bots that use natural language processing (NLP) algorithms to learn from conversation with human beings and imitate the language patterns while providing answers. This frees up customer service employees to focus on more complicated tasks. These are the most widely used type of AIaaS right now.
- Cognitive computing APIs. Short for application programming interface, APIs are a way for developers to add a specific technology or service into the application they are building without writing the code from scratch. Common options for APIs include NLP, computer speech and computer vision, translation, knowledge mapping, search, and emotion detection.
- Machine learning frameworks. These are tools that developers can use to build their own model that learns over time from existing company data. Machine learning is often associated with big data but can have other uses – and these frameworks provide a way to build in machine learning tasks without needing the big data environment.
- Fully-managed machine learning services. If machine learning frameworks are the first step towards machine learning, this option is a way to add in richer machine learning capabilities using templates, pre-built models, and drag-and-drop tools to assist developers in building a more customized machine learning framework.