Low-code involves designing a framework without developing a single line of coding because you intend to do so, providing possibilities for creativity. Low-code systems are distinct from non-code platforms. In no-code, the solution is designed entirely by drag-and-drop elements, leaving virtually no room for customization. The concept was developed over years of limitations in designing and development.
Over the years, computer scientists, including Machine Learning researchers, have argued that heavy programming solutions are far more vulnerable to glitches, including coding-related errors. In addition, there are collections of codes that are redundant throughout the Machine Learning model. This renders the code structure complicated and challenging to manage.
Low-code Machine Learning Framework produces modules, templates, including microservices that replace the redundant code structure, thus reducing the burden of algorithms. This brings benefit to developers and data scientists by allowing them a wide variety of valuable experience to design, train and deliver models faster. However, a low code framework cannot completely replace the code created by typing. But it does help the developers to take control of the machine learning blocks to recreate prototypes.
How it helps businesses grow?
Customized data science tools are ideal for solving complex market issues. You can use Python, R, and D3, among others, to build anything you need, although at the expense of speed and far more time spent on the market. But at the other end, we have technologies such as Excel, SQL, and Power BI. These technologies provide accelerated growth on a volume but provide very minimal versatility. Though the data collection, analysis, and visualization process are quick, you can’t just create whatever you want.
Why Low/No Code is popular?
The low-code methodology to production tackles a need for Machine Learning professionals who are just not programmatically oriented. This condition is backed by a survey by Mendix, which shows that 24% of low-code users have almost 0% programming experience. Organizations with fewer qualified data scientists and machine learning platforms departments have the luxury of choosing data set and evaluation parameters, whereas the extra effort is being transferred to the algorithm. Processes such as business data visualization, deep learning enabled image recognition, and others can be easily programmed using the low/no code methodology. It allows for unsupervised deep learning that can be developed by anybody. It is an ideal tool for startups and smaller enterprises with budget constraints to manage a whole team of developers.