No-code AI: How it differs from low-code AI and Auto ML

The adoption of artificial intelligence (AI) has increased multifold over the past few years. A Deloitte survey indicates that enterprises are bullish on cognitive and AI technologies, with expectations that they will transform both companies and entire industries. Demand for integrative AI has increased across sectors, leading to the rise of tools like AutoML, low-code AI, and no-code AI. 

No-code AI is a relatively new concept that is aimed at democratizing the use of artificial intelligence. The technology enables users with no technical knowledge and experience to design applications and websites without writing any codes. The no-code AI platforms often offer a drag-and-drop interface to deploy AI and machine learning (ML) models. It provides a quick way to test ideas and build new projects and products much faster.

Similar to the concept of no-code AI are Auto ML and low-code AI. All three platforms enable individuals without formal training in software development to develop AI solutions without much investment. But, they differ from each other in critical components and features. In the coming few seconds, we will learn how these three vary from one another.

Low-code AI vs no-code AI

As the name suggests, low-code AI is a limited version of no-code AI. The low-code AI tools help deliver apps and web applications faster but may require some knowledge of coding. Unlike no-code AI tools, low-code AI tools do not completely eliminate the need for coding. To implement low-code AI, users do not require writing code line-by-line. Instead, they can draw flowcharts in a visual editor wherein the code will be automatically developed. Also, the concept of low-code AI was introduced in 2011, earlier than no-code AI which gained momentum in 2021.

Auto ML is defined as the process of automating the task of applying machine learning to real-world problems. AutoML software increases the efficacy of data science and brings more transparency to the machine learning process. Similar to the no-code AI, the AutoML tool allows anyone to build machine learning models without having the technical expertise. Having said that it is important to know here that AutoML is one of the no-code AI tools similar to the drag and drop, flow builder, and pre-trained APIs.

Another point worth mentioning is that AutoML tools may be good at automating tasks, but they lack an understanding of the business context. For that, one may still require additional skills like model interpretability, data skills, feature skills as well as model deployment skills. This is why AutoML tools are considered complementary tools for data experts, rather than standalone tools for non-experts. 

You can leverage the power of AI and Machine Learning to build on top of existing applications, create smart solutions and improve your data science capabilities without having to write a single line of code. Start your learning journey today with the No Code AI and Machine Learning Program by MIT Professional Education – Digital Plus Programs. The 12-week program is designed and taught by renowned MIT faculty and experts in advanced technologies.

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