The short answer is yes.
Traditional machine learning requires you to know software programming, which enables data scientists to write machine learning algorithms. And that takes a lot of time, resources, and manual labor.
But it’s clear that in order to stay competitive, businesses need to leverage this kind of technology.
So, what happens when you’re not a data scientist and can’t build complex machine learning models? Or if you don't have the budget to hire a team?
Can you do machine learning without code?
The Machine Learning Process
To answer this question, we need to first look at what it takes to build machine learning models.
Machine learning has been incredibly hard for many businesses to adopt. Historically, you’ll see large businesses who have the budget adopt it. That’s because the machine learning process is long, expensive, and requires deep technical knowledge and skill sets.
A traditional machine learning process, from start to finish, looks like this:
As you can see above, this is a really long and complicated process. To do all of this requires data scientists and machine learning engineers.
The challenge? Finding (and retaining) the kind of talent to build these models is hard to come by. This is because our data has grown so much over the years thanks to the plethora of technology. Which means there is way more data than there is talent to unpack it all.
Related Reading: Reasons to Master No-code Machine Learning
The demand is so high that even individuals who have pursued computer science and technical programs at universities are being thrust into performing demanding data analytic positions in the workplace.
According to Glassdoor, the average salary for a data scientist working in the U.S. is $113,436. What’s behind this price tag? Low supply, high demand, and the skill sets associated with the role.
In fact, McKinsey predicted the U.S. would experience a shortage of 250,000 data scientists by 2024.
Off the Shelf Approaches
This means that if you’re not a large business with a budget for data science teams, you’ll likely need to go the off-the-shelf route.
The good news is that there are many on the market—and they are generally affordable.
The bad news is that more often than not, these tools don’t align well with the way most businesses are structured.
This is because they’re usually designed for analysts and data scientists, people with highly advanced, technical degrees. Which makes sense. But if you consider that the end users of all that data aren’t typically well-versed in data (such as Marketers, HR, sales teams, and more) then traditional BI tools are actually quite inefficient: You have people at the frontline of decision-making that need to make data-backed decisions, but rely on data scientists to use BI tools.
That creates a lot of backlog and bottleneck. Data scientists end up spending an inordinate amount of time doing things for other teams, like building monthly reports, instead of getting to use their advanced degree. As well, traditional BI solutions have inefficient workflows.
Most well-known solutions consist of multiple systems and applications, which force users to exit their current workflow and jump into another application to secure valuable data.
As well, many BI tools, even if they have machine learning embedded, are not designed for business users. Traditional vendors often try to cover the complexity of their solution with self-service options and features, but users continue to feel like they need an advanced engineering or computer science degree to navigate them.
So while BI tools have many benefits, they’re not built for today’s modern business, and actually create a lot of inefficiency.
Democratizing Data with No-code Machine Learning
What businesses actually need are tools that allow anyone to leverage the power of data and predictions. That would not only eliminate bottlenecks and free up bandwidth for the data science team, but it would also increase efficiencies and decision-making.
This is why no-code tools are causing such a stir in the market. They allow anyone to create machine learning models - without code.
What Does No-code AI Mean?
No-code AI is a category in the AI landscape that uses a code-free development platform. Most no-code platforms provide a drag-and-drop dashboard that allows users to upload or import their data so they can build highly accurate machine learning models in a matter of seconds.
This means that anyone, regardless of their knowledge on regression or forest algorithms, can build a model and generate predictions.
For instance, a manager experiencing employee turnover could leverage upload data to better understand causes for attrition and implement retention strategies.
In just a few clicks, a fully trained AI model is generated, predicting employee turnover in seconds. That manager can understand which of their employees are at risk of leaving and get a hint of what interventions could be implemented to reduce attrition.
Of course, predicting employee turnover is only one example - the opportunities and use cases of no-code are quite limitless and prove their business value tenfold. One Gartner Magic Quadrant report predicts that by 2024, the power of AI will be democratized, with as much as 65% of application development will be done on no-code/low-code platforms.
No-code Machine Learning Platforms
As you can see, you don't need to go the traditional route to harness the power of AI. No-code AI enables businesses without a data science team to become data-driven. Teams can train and deploy models with minimal to no coding knowledge in significantly less time while staying economical.
This shorter process means you can quickly predict metrics like churn, loan-to-value, the tenure of a contract—all in a matter of seconds.
With the rise of no-code platforms, the barrier to entry for AI is lower than ever before. Which is great for you and your team, but with new and more innovative products coming out every day, not all of them have staying power.
So, when looking for no-code machine solutions, be sure to keep a few questions top of mind:
- How fast can it generate predictions? Your no-code machine learning platform should allow you to generate predictions in less than a minute. In fact, the best in the industry let you make predictions in seconds.
- Will it scale as you grow? A platform’s ability to adapt and grow with you is crucial. How many predictions can you make with it? How many users or seats are you allotted?
- Do they have documentation and support? While no-code platforms are designed to be intuitive and let your team figure things out on their own, the vendor you select should be readily available to help answer any questions, and have robust educational materials and a strong system of support services.
- What kind of reputation do they have? Look at sites like crunchbase to see who is invested in them. For instance, if they have reputable investors, you know that platform is credible. Knowing who their investors are will help you believe (or not) their claims to being the fastest in the industry.
Machine Learning Without Code Generates Business Value
So, back to our original question: Can you do machine learning without coding?
Yes, you can. And actually, we think you should.
Data informs insights, and insights drive decisions. When data is democratized, everyone in your organization is empowered to find, understand, collaborate, and act on the data they need.
That translates into faster decision making, which makes for more agile teams, and increases your overall business value.
Not only that, it frees up your data scientists team’s time (or, gives you the power of a data science team if you don’t have one).
Machine learning without code is entirely possible thanks to the plethora of tools available in the market. In fact, they’re changing the way whole industries run. To see what kinds of use cases no-code tools can be applied to, check out our library of case studies.
To see a no-code tool in action, be sure to book a demo with our team today!