fbpx Why is a No-Code Recommendation System worth a try? | Leapfrog Technology

Why is a No-Code Recommendation System worth a try?

Lately, in the tech world, no-code systems have been getting immense attention, and for good reasons. These types of systems allow the user to develop software with a much easier drag and drop interface, as opposed to the traditional coding using programming languages.

No-code systems have been gaining popularity because they provide a long list of advantages among which less manpower, less time, and an easier development process are the most significant ones. Some really popular examples of no-code systems are web design tools like WordPress, Webflow, and software development tools like Airtable, Nintex, Appy Pie, etc.

These days, no-code systems are turning out to be more and more advanced. And they are already being developed for more advanced technologies like AI and blockchain. In this blog, we will dive into the world of recommendation systems and why no-code recommendation systems are the future of AI. Let’s get started!

Non-programmer friendly

We know that building a recommendation system, or any system for that matter is not a one-man job. Programmers, non-programmers, business specialists, and executives are involved when it comes to that. It is not necessary that everyone knows how to code. In fact, in many cases, only a few members will know the actual programming portion.

No-code recommendation systems are advantageous in such cases because people who have access to the data will be able to use the system when the tech guys are not available or working on something more important. For example, in the case of Caboom, any executive, business owner, or product manager who has access to the data can use the system for testing the feasibility of the recommendation system without anyone’s help.

The process which traditionally would require some manpower, more time, and additional expenses, will be done much faster with the help of a UI tool.

AI Democratization

AI democratization is a heavy term with a simple meaning: making AI accessible to everyone in an organization. AI is a revolutionary tool, and it should be accessible to anyone, specially non-AI people who are involved in a primarily AI-focused organization. The no-code recommendation system is the ultimate next step towards this.

This kind of simple UI application will make sure that the power of AI is being utilized to its full potential with every techie and non-techie working hand in hand with it.

Efficiency and Productivity

No-code systems are much more efficient than the traditional methodologies because of their simplicity and easy drag and drop features. If you have the basic knowledge, you will be able to design, develop, or test your recommendation system in much less time as well as less cost and manpower.

Along with that, a small team will be able to build your application using a no-code platform and smaller skill set, while more resources can be focused on bigger more complex ML projects at hand. Thus, it is more efficient as well as increases the productivity of the whole team.

AI is not just about technology and engineering, but also about art and nuance. People have to go through a lot of heavy lifting to understand the technical aspects. The goal of such no-code recommendation systems is to do that heavy-lifting for you so that you can just focus on the art and nuances of AI.

While the whole concept of the no-code recommendation system is still in its infancy, it does show a promising future. We can expect more advanced no-code AI systems in the future and soon, it will be possible to build AI systems with a lot less effort, cost, and other resources.

*Caboom is an internal project of Leapfrog.
*Also published at caboom.ai.

Drishya Bhattarai

Drishya is a Digital Marketer at Leapfrog. She is interested in SEO, digital marketing, content creation and Designing.

More in Blogs

Customer Segmentation Using RFM Analysis Artificial IntelligenceInsights

Customer Segmentation Using RFM Analysis

We live in a dynamic world, and people are seemingly looking for growth, and many depend on establishing and running

Read more
Using FAISS Advanced Concepts to Speed Up Similarity Search in Recommender Systems  (Part II) Artificial IntelligenceInsights

Using FAISS Advanced Concepts to Speed Up Similarity Search in Recommender Systems (Part II)

Continued from the last blog. Improving on Flat Indexes Beyond the flat indexes that perform exhaustive searches, FAISS also has

Read more
Using FAISS basics to Speed up similarity search in Recommender Systems (Part I) Artificial Intelligence

Using FAISS basics to Speed up similarity search in Recommender Systems (Part I)

Measuring the similarity between vectors or arrays is a calculation that we encounter often while developing recommendation systems or other

Read more