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Artificial Intelligence (AI) is in a fast lane and moving toward mainstream enterprise acceptance, but, at the same time, another technology is making its presence known: low-code and no-code programming. While these two first dwell in different areas within the data stack, they offer some interesting possibilities to work together to make data processing and product development widely easier and streamlined.
The purpose of low-code and no-code is to make it easier to create new applications and services, so that even non-programmers – that is, knowledgeable workers who actually use these applications – can create the tools they need to accomplish their own tasks. They work primarily by creating modular, interoperable functions that can be mixed and matched to suit a variety of needs. If this technology can be combined with AI to guide development efforts, it cannot be said how productive the enterprise workforce can be in just a few short years.
Venture capital is already flowing in this direction. A startup called Sway AI has recently launched a drag-and-drop platform that uses open-source AI models to enable low-code and no-code development for new, intermediate and specialized users. The company claims that this will allow organizations to quickly put new tools into production, including intelligent ones, while at the same time promoting greater collaboration among users to expand and integrate these emerging data capabilities both efficiently and highly productively. The company has already developed its general platform for specific use cases in healthcare, supply chain management and other areas.
The contribution of AI to this process is basically the same as in other fields, says Jason Wong of Gartner – i.e., rotate, to perform repetitive tasks, which include development testing processes such as performance testing, QA and data analysis. Wong noted that the use of AI in no-code and low-code development is still in its infancy, with big hitters such as Microsoft keen to apply it in areas such as platform analysis, data anonymity and UI development, which is currently declining. doing. Lack of skills that is preventing many initiatives from achieving product-ready status.
Before we start dreaming about an optimized, AI-powered development chain, however, according to developer Anouk Dutri, we need to address some practical concerns. For one thing, abstracting code into composible modules creates a lot of overhead, and introduces delays in the process. AI is increasingly gravitating towards mobile and web applications, where even a delay of 100 ms can take users away. This shouldn’t be a problem for back-office apps that tend to stay quiet for hours, but after that, it’s unlikely to be the right area for low- or no-code development.
In addition, most low-code platforms are not very flexible, as they often work with pre-defined modules. Cases of AI use, however, are generally based on highly specialized and available data and how it is stored, conditioned and processed. So, in all likelihood, you will need a custom code to make the AI model work properly with other components in a low / no-code template, and this can cost more than the platform. The same dichotomy also affects functions such as training and maintenance, where the flexibility of the AI runs at low / no-code relative rigidity.
Adding doses of machine learning to low-code and no-code platforms can help them, however, and also add the much-needed dose of ethical behavior. Dattaraj Rao of Persistent Systems recently highlighted how ML can allow users to run pre-candid patterns for processes such as feature engineering, data cleansing, model development and statistical comparison, all of which are transparent, understandable and predictable modes. Helps.
It may be an exaggeration to say that AI and no / low-code are like chocolate and peanut butter, but there are good reasons to expect that they can enhance each other’s strengths and reduce their vulnerabilities in a number of key applications. As the enterprise becomes increasingly dependent on the development of new products and services, both technologies can overcome many of the barriers that currently impede the process – and this is likely to be the case whether they are working together or independently. .
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