You’ve probably heard of machine learning, and you’ve almost certainly experienced it. Machine learning algorithms help power everything from Netflix’s personal movie recommendation engine to Google Search and Translate, and research suggests that companies will invest $12.3 billion in machine learning by 2026, up from the $2.5 billion spent in 2017.
While large enterprises invest billions to develop machine learning (ML) capabilities, smaller companies and individuals can sometimes feel that machine learning is out of their reach. After all, the average salary for a machine learning engineer is about $140,000/year (source), and learning how to code isn’t exactly a small task.
Luckily, one emerging technology trend, machine learning as a service (MLaaS), is removing barriers such as time, budget, and even coding expertise to make the power of machine learning available to everyone. Offering ready-made tools that can be easily adopted and fitted to various business needs, MLaaS is being used by business leads and front-line workers in an array of industries.
So how does MLaaS work?
Coding expertise is perhaps the largest barrier to creating apps. Software development traditionally includes everything from data collection and debugging to resource provisioning and security, which is (for good reason!) a full-time job in and of itself. Machine learning applications are no different, and require a foundation of complex coding before app development can begin.
No-code development platforms automate the bulk of this sophisticated behind-the-scenes work so that non-technical users, aka citizen developers, can tackle the business task at hand without touching a line of code. This means that teams and individuals can autonomously build and deploy apps made by them — and for them — in a matter of days, not months.
Whereas traditional models of app development require heavy cross-functional communication and weeks of iteration, no-code development puts the power to solve problems into the hands of those who know the problem best. Because no-code requires zero technical know-how, MLaaS is accessible to a much wider group of people and teams within an organization than traditional machine learning implementation.
With a no-code platform, anyone — regardless of their technological prowess or background — can build robust applications that are driven by machine learning algorithms to solve problems, increase productivity, and deliver a healthier bottom line.
Like no-code development, MLaaS is not limited to any particular group, and is used by industries from manufacturing to healthcare to empower non-technical employees to improve their processes with powerful digital technology.
As we’ve seen, MLaaS allows anyone in an organization to digitize routine work and automate tasks with apps that would have otherwise been too costly or time-consuming to develop. From HR and finance to sales and marketing, people in any area of a company can easily build apps that are customized for their team, process, data, and goals.
With no-code, machine learning doesn't need to be expensive to deliver enormous value. This democratized solution saves companies time, money, and resources, while empowering every employee to do her best work.
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