Fundamentals of AIaaS and AIPaas (AI-as-a-Service and AI Platforms-as-a-Service)

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There are many factors that have started making businesses restless and eager to dive into the newest intelligent technologies for their Data Management practices. The business operators have sighed with relief knowing that they no longer have to engage dedicated talents for advanced model development or cloud infrastructure planning. The idea of “managed (hosted) Data Management” suddenly became the No. 1 priority of all businesses. From public utility sectors to finance and healthcare, smart solutions flooded all sectors.

Factors that Triggered the Growth of AI-as-a Service

Some major events have taken place in the information-technology world in the last few years:

  • The cloud platform surfaced with a variety of affordable options for enterprise Data Management, moving from platform to infrastructure to data-centric services.
  • Data storage technologies became cheap and reliable.
  • Streaming devices and technologies (IoT) became the new normal in global businesses.
  • AI technologies like machine learning (ML), deep learning (DL), natural language processing (NLP) grew by leaps and bounds.
  • The availability of semi or fully automated Data Management, analytics, and BI products removed the need for data scientists in day-to-day business tasks. This factor signaled the empowerment of the ordinary business user dreaming of being the next citizen data scientist.

From 2018 till this year, the Software-as-a-Service (SaaS) market spiked from $5.6 billion to $133 billion. Closely following, the advanced technology platforms-as-a-service market globally is forecast to reach about $11 billion by 2023 and surpass  “$88,500 million by the end of 2025.” The four players in this domain — Google, Amazon, Microsoft, and IBM — have been engaged in a race for AI stardom for awhile now.

Why AI as a Service Will Take Off In 2021 suggests these four are able:

“To make AI-as-a-Service a reality for those looking to use AI for everything from customer service to robotic process automation, marketing, analytics, predictive maintenance — you name it.” The article says, “in 2020, AI won’t just be nice to have, it will be a necessity.”

AI Off the Shelf: Repackaged AI Solutions

To fulfill the business user’s rising demand of automated AI products for analytics or BI requiring minimal technical knowhow, the software vendors have responded with pre-packaged, AI-enabled Data Management services. These products and services can be grouped under the term “AIaaS,” and can be quickly deployed and used in any business environment.

AIaaS solutions can be easily integrated with the cloud platform, which is another reason global businesses of all shapes and sizes with limited, in-house data infrastructures have readily adopted AIaaS services for their Data Management needs. Get Ready for the Emergence of AI-as-a-Service, offers an interesting argument behind the quick adoption of “as a service” technology platforms. This author thinks the biggest demand of businesses was “to drive business outcomes with unmatched efficiency,” which only automated machine models can deliver as they surpass human accuracy, speed, and efficiency levels by several notches.

The idea of hosted Data Management services picked up a few years ago when cloud service providers started offering custom solutions depending on the exact needs of particular businesses. The Rise of Artificial Intelligence as a Service in the Public Cloud indicates that though initially public cloud providers used AI capabilities to acquire customers to their platforms, they gradually moved their services from being platform-centric to data-centric.

Data security, data privacy, and Data Governance were common concerns among all business owners and operators using cloud platforms, but the later hybrid cloud or private cloud offerings resolved those issues. The best part of cloud-based data centers is that they offer tremendous scaling opportunities, enhanced operational efficiency, and high productivity.

Ready-made AI-powered data platforms have brought big data analytics, IoT, edge computing, and other advanced technologies to businesses’ doorstep. Jay Chapel presented his thoughts in SaaS vs. PaaS vs. IaaS: Where the Market is Going?

The Difference between AIaaS and AIPaaS

AI-as-a-Service typically refers to any advanced software package with built-in intelligence to mimic or replicate human thinking. In case of Data Management software, prebuilt machine learning (ML) or deep learning (DL) algorithms are integrated with the software system for advanced trends analysis, pattern matching, or for predictive analytics. As human data scientists become scarce and expensive, these software packages can quickly meet the day-to-day Data Management needs of businesses. Additionally, because they are priced economically, medium or small businesses can afford them.

AI Platform-as-a-Service, on the other hand, indicates an end-to-end solution like a cloud platform, where business users can avail required services on a pay-per-use or pay-per-service basis. AIPaaS often include managed sub-services and third-party APIs, thus providing a complete, intelligent Data Management platform requiring little human (data scientist) intervention.

In AIPaaS, business customers can access a host of data services without investing a penny on costly in-house infrastructures or full-time technical manpower. To summarize it in the words of a blog post, Enterprise AI is a part of “a range of business applications, such as expert systems, speech recognition and image recognition.”

Larger Benefits of AIaaS and AIPaaS

Here are some immediate benefits of adopting an AIaaS or AIPaaS solution:

  • Minimal investment in IT infrastructures
  • The solution deployment time is short
  • Minimal dependence on technical manpower
  • In-house data scientists are free to pursue highly advanced and selective Data Science tasks
  • Cloud service providers offer affordable and custom pay structures
  • Pre-built algorithms make complex Data Management accessible to the ordinary business user
  • On-demand insights empower business users with a competitive edge
  • Multi-solution platform like Google Cloud brings a variety of benefits like advanced analytics, speech recognition, and translation services 

If there is one drawback in the AIaaS or AIPaaS services, it is the lack of Data Governance, as reliance on third-party vendors poses a threat to data security and data privacy. Going forward, researchers and solution vendors will have to work together to develop breach-proof data platforms.

AIaaS Market Report

Artificial Intelligence as a Service (AIaaS) Market Discussed in a New Research Report provides an in-depth discussion of “market share and size, trends, and forecasts, and growth opportunities” in terms of “product types, applications, key manufacturers, key regions and countries.”

Closing Statement

According to AI as a Platform-as-a-Service Transforming Data With Intelligence, the future of AI service platforms lies in aligning specific solutions with specific use cases. An example is the use of AI services for autonomous vehicles, where different components manufacturers have implemented AI capabilities to develop one comprehensive and integrated autonomous transport.

Image used under license from Shutterstock.com

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