AI-as-a-Service

AI-as-a-Service

What is AIaaS 

Artificial intelligence as a service refers to off-the-shelf AI tools that enable companies to implement and scale AI techniques at a fraction of the cost of a full, in-house AI.

The concept of everything as a service refers to any software that can be called upon across a network because it relies on cloud computing. In most cases, the software is available off the shelf. You 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 a long time, artificial intelligence was cost-prohibitive to most companies:

• The machines were massive and expensive.
• The programmers who worked on such machines were in short supply (which meant they demanded high payments).
• Many companies didn’t have sufficient data to study.

As cloud services have become incredibly accessible, AI is more accessible: companies can gather and store infinite data. This is where AI-as-a-service comes in.

Now, let’s detour into AI so that we have the right expectations when engaging with AIaaS.

The growth of AIaaS

For companies that can’t or are unwilling to build their own clouds and build, test, and utilize their own artificial intelligence systems, AIaaS is the solution. This is the biggest draw: the opportunity to take advantage of data insights without needing the massive up-front investment in talent and resources.

Like other “as a service” options, the same benefits apply with AIaaS:

Staying focused on core business (not becoming data and machine learning experts)

• Minimizing the risk of investment
• Increasing the benefits you gain from your data
• Improving strategic flexibility
• Making cost flexible and transparent

Benefits & drawbacks of AIaaS

Like any other “as a service” offering, AIaaS brings value to companies without costing huge amounts. But there are also distinct drawbacks to using a cloud-based AI system that no business should ignore

Types of AIaaS

Common types of AIaaS include:

Chatbots & digital assistance

These can include chatbots that use natural language processing (NLP) algorithms to learn from conversations 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 types of AIaaS today.

Cognitive computing APIs

Short for application programming interface, APIs are a way for services to communicate with each other. APIs allow 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:

Machine learning frameworks

ML and AI frameworks 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.