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?

No code needed: basic features of democratized machine learning

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.

MLaaS and the power of problem-solving

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.

  • Faster time-to-value: Business leads and process owners often know best where help is needed on the ground, but can’t wait weeks or months for diverse teams to coordinate, build, test, and iterate on a niche application built with them in mind. MLaaS makes it easy for process owners to build apps that do everything from helping users interact with data more quickly through natural language processing to interpreting qualitative categories of new data, within days.
  • Process improvement: MLaaS can also help non-technical process owners significantly streamline workflows and existing processes. For example, MLaaS empowers any employee to build predictive models that can generalize from historical app data, providing the ability to forecast values and predict trends.
  • Time reallocation: From supply chain optimization to inventory management and predictive maintenance, businesses rely on software development and machine learning to get things done. When the power of machine learning is distributed across an organization, the role of planning and execution moves from the IT and development teams, to the  people who know their challenges best. This frees up time for IT teams without sacrificing technical ability and growth.

Tools that put experts in control

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.

AppSheet machine learning resources

Create apps on AppSheet with the power of machine-learning built in. Exploring these resources to learn more: