Advantages and Disadvantages of Low-Code & No-Code AI Platforms

Apac CIOOutlook | Friday, December 10, 2021

AI can aid in predicting churn rates, the analysis of reports, the addition of smart suggestions, the automation of invoicing, and much more.

Fremont, CA: Low-code/no-code AI may be applied in every industry to improve workflows, anticipate attrition, and provide suggestions. Simple picture categorization AI models may get quickly constructed using low-code/no-code platforms and utilized in factories to distinguish between quality and defective items or in the healthcare business to identify whether individuals are wearing masks within the facility. The possibilities are endless.

Businesses in more data-driven industries, such as marketing, sales, and finance, will profit from low-code/no-code AI systems. AI can aid in predicting churn rates, the analysis of reports, the addition of smart suggestions, the automation of invoicing, and much more.

Advantages of low-code/no-code platforms

  • Accessibility

Low-code/no-code platforms enable non-technologists or enterprises to construct AI systems from the ground up, making AI more accessible to a broader range of organizations.

  • Speed

Because low-code/no-code AI platforms frequently include pre-built AI models, project templates, and ready-made datasets, labeling and iterating the data takes considerably less time, substantially boosting model development.

  • Scalability

AI executes duties for many (if not hundreds) of users, saving the firm time and resources. Furthermore, the servers are automatically scaled up or down based on the demand, and it is quite simple to monitor the workload and progress.

Disadvantages of low-code/no-code platforms

  • Security

Some platforms may fail to develop access protocols, which is a problem for businesses where security is paramount. It's good to read the terms and conditions to understand how and where the data will get handled.

  • Lack of customization

Low-code/no-code platforms, while simple and fast, are typically restricted in functionality since they can handle a specific problem. It is challenging to come up with out-of-the-box, more complicated solutions. Business requirements shift like the wind, so what do users do when they have outgrown a certain solution or functionality?

  • Requires consultation or training

The ML engineer, human resources professional, and marketing intern should all be able to use the low-code/no-code platforms equally, but this isn't always the case. Because the end-user of an AI platform is already an ML engineer, the rest will require extensive training and consultations to become acquainted with AI processes.