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Reasons to Master No-Code Machine Learning

Nirman Dave

If you Google “learn machine learning,” you’ll find a bunch of guides, online courses, and such that walk you through the coding languages of ML and the processes it takes to solve data predictions. You even get this futuristic robot with the list of steps. Basically, you conclude very quickly it takes a lot of time to learn technical machine learning. 

And it does. Mastering ML is super hard and requires a time investment if you want to apply it to solving problems for your business.

However, something that doesn’t come up that often in Google, is “no-code machine learning.” Compared to the keyword “learn machine learning,” which has the volume of around 2K searches/month (according to Ahrefs), “non-technical machine learning” Ahrefs doesn’t even have enough data to get an accurate search volume—meaning the search volume is very low.

This is crazy to us, being a no-code machine learning platform because ML doesn’t just have to be reserved for technical programmers anymore. Analysts now have the power of data predictions and the ability to move quicker, cheaper, and more creatively with no-code machine learning. 

Traditional ML vs the No-Code ML Process

The Traditional Machine Learning Process

When searching for how to approach the machine learning process you may come across this post (or a post like it) and get close to ten steps on how to collect data, build a model, train it, improve it, etc. 

Typically the traditional model looks like this:

The typical traditional machine learning process.

The No-Code Machine Learning Process

No-code ML simplifies this process into this: 

The no-code machine learning process.

This is a much simpler route for someone looking to make data predictions without taking the time to master technical machine learning skills. 

With drag and drop data predictions, you can simply edit your queries by replacing identifier and prediction columns and setting aside columns you don't want to use.

This allows you to quickly predict metrics like churn, LTV, the tenure of a contract etc.

We’re Creating a New Approach to Machine Learning

With a tool like Obviously AI, you can become a data-driven company without having a data science team or scaling one. We’re challenging the traditional approach of learning technical machine learning and introducing more accessible non-technical machine learning.

With no-code machine learning you: 

Become data-driven without a data science team - Most companies that want to be data-driven don’t often have a data science team or cannot scale one. This means finding data science talent and shifting around a budget to offer salaries to data scientists or analysts become common roadblocks in their day-to-day work. In such scenarios Obviously AI, a no-code ML tool, can offer a great alternative and provide results in seconds rather than days/weeks.

Can create ML-driven products and scale them - Customers now want personalization, efficiency, and content and product curation. To do that, products need data input and output that appeals to the user’s needs. Data predictions can help your product improve UX and allow you to make more informed business decisions about your product.  

Eliminate costs while improving profit - Referencing our post on dynamic pricing, this is just one example of many of how to apply ML to increase profit opportunities. You can also use historical data to make predictions on where to cut costs and improve customer retention.

See how Tectonic London used no-code predictions in their app development.

No-Code Machine Learning is Fast, Simple and Economic

As you can see, the no-code machine learning process is much shorter and simpler than the traditional process which also requires the learning of technical skills. With a no-code machine learning platform, you can do all of the same things—except without the expensive team of data scientists and improve business and product decisions quicker. 

Most importantly, no-code machine learning allows you to be more creative with your data. If you’re interested in learning more about creativity and machine learning, keep reading our blog as that’s our main mission of creating these terrific posts for you. 

OR get started with no-code machine learning here to make predictions from your data.

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