Learn why businesses should use AI-as-a-Service solutions.
For any application that is available on-premises, it is almost certain that it will eventually also be available as a cloud-based service, delivered on demand by a cloud service provider. A somewhat recent addition to the growing field of cloud-based services is AI as a Service (AIaaS). With AIaaS, companies can reap the benefits of AI without having to invest in hardware and software upfront. And in the case of AI, the savings can be significant.
After decades as fodder for science fiction movies, the use of artificial intelligence in business has exploded. Businesses use AI for everything from customer service and marketing to process automation, security, and business and sales forecasting. In fact, a survey by strategic advisors NewVantage found that nine out of ten top companies are continuously investing in AI. A 2019 study by IT researcher Gartner found 37% of organizations actually made use of AI in the workplace in 2019.
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However, for small and medium-sized businesses, the same Gartner report, only 29% said they have adopted AI. This is at least somewhat influenced by the knowledge that specialized AI hardware is required and often prohibitively expensive. This is because a generic off-the-shelf server could be used, but due to the massive processing power required, it is not ideal and productivity would grind to a halt.
And that’s just the investment for hardware. Then there’s the software, programming, and training of models, which require specially trained data scientists who pay significant salaries. With AIaaS, businesses of all sizes can reap the benefits of AI research, machine learning, and analytics on demand and via the cloud.
When should companies use AIaaS?
Like any other technology, AI has been introduced slowly and incrementally. Companies dip their toes in the water before diving headfirst into it to try it out and see if it lives up to its promise. So the initial rollout of early AI projects is generally measured and modest. Smaller companies in particular are risk averse.
AIaaS is especially valuable for companies that don’t expect a lot of AI work from the get-go. AI breaks down into a two-step process: training and inference. The training part is the computationally intensive part, but the inference requires much less power and can be handled with a much less powerful, non-dedicated processor.
Now let’s say you plan to deploy about two or three AI projects and you have chosen to invest in specialized hardware. Because you cannot reuse an AI training server as a general purpose database server, it remains unused.
Conversely, if you run multiple AI projects each year, consider taking a hybrid approach and investing in an on-premises system. This is because cloud services use a pay-as-you-go model for all the computing power needed to ingest and process data, as well as all associated storage, database, networking, and analytics applications. Ambitious AI projects generate huge amounts of data. AIaaS projects, known as “data gravity”, can multiply the requirements for additional capacity and services, driving up costs. This can easily inflate the cloud service provider (CSP) bill and ultimately make it more economically viable to bring these workloads on-premises.
How AIaaS is democratizing AI
There are several programming languages for AI, from the common and ubiquitous (Python, C++) to the esoteric (R, Rust). This can be challenging for a non-data scientist, who may not have coding skills or understanding of data science beyond the basics. And all too often, non-data scientists are tasked with owning AI projects because there simply aren’t enough skilled programmers and data scientists to meet the ever-increasing demand for their skills.
Fortunately, CSPs that offer AIaaS services also offer no-code infrastructures for non-programmers. No-code tools and services are tools and services that allow people to build applications without having to program them in the traditional way of writing, testing, and debugging source code. Instead, the core functionality is created through visual aids, much like a flowchart, where actions are taken based on pre-set conditions. If you ever use Microsoft Visio, you’ll have an idea of how this works.
No-code allows business users to do the work of programmers, but the downside is that the applications are often simplistic. If you want fine-grained, precise control and action of complex AI models, you still need to program the application.
But no code is still very good for getting started writing simple AI apps, easing the burden on data scientists who have much more demanding tasks ahead of them, and perhaps writing a simple chatbot.
Finally, the pros and cons of an AIaaS approach or an on-premises/hybrid approach to AI should be carefully considered, taking into account cost, time, and workforce specialization. For those just starting out or undertaking a limited number of AI projects per year, the benefits of AIaaS can far outweigh the alternatives.
Phil Brotherton is the vice president of solutions and alliances at NetApp.