Low Code for Data Science

Low Code for Data Science

3 Reasons Why You Need Low-code Platforms For Data Science Solutions  in TowardsDataScience

Low-code ML applications help address the challenges of model maintenance, time-to-market, and talent shortage

Organizations across industries are turning to data and analytics to solve business challenges. A survey by New Vantage Partners found that 91 percent of enterprises have invested in AI. However, the same study found that just 26 percent of these firms have AI in widespread production.

Organizations are struggling to solve business challenges with AI. They find that building machine learning (ML) applications takes time and requires expensive maintenance and talent that’s in short supply. Leaders say that over 70% of data science projects report minimal or zero business impact.

Here’s how low-code ML platforms can help tackle these challenges.

What is low-code, and why this craze now?

Low-code is a software development approach that leverages a visual user interface to create applications instead of traditional hand-coding. For decades, developers built applications by writing thousands of lines of code from scratch, often round-the-clock.

Building software solutions using low-code falls somewhere in the continuum between programming from scratch and buying off-the-shelf. It brings the best of both worlds by balancing flexibility and time-to-market.

A low-code development platform (LCDP) is considered quicker to build, economical to maintain, and developer-friendly because of its visual approach.

Low-code tools empower enterprises by democratizing software development. Today, anyone with a business interest and basic technology skills can build an app using low-code technology. According to Gartner, by 2024, more than 65 percent of all app development will be on low code. Globally, the low-code market is projected to reach $187 billion by 2030.   ... '