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Artificial intelligence (AI) is gaining momentum and moving towards corporate adoption, but at the same time, another technology is making its presence felt: low-code and no-code programming. While these two initiatives cover different areas within the data stack, they nevertheless present some intriguing opportunities to work together to greatly simplify and streamline data processes and product development.
Low-code and no-code are intended to make creating new applications and services easier, so much so that even non-programmers — that is, knowledge workers who actually use these apps — can create the tools they need to create their own. perform tasks. They primarily work by creating modular, interoperable features that can be combined and adapted to a wide variety of needs. If this technology can be combined with AI to help guide development efforts, there’s no telling how productive the enterprise workforce can become in a few years.
Venture capital is already starting to flow in this direction. A startup called Sway AI recently launched a drag-and-drop platform that leverages open-source AI models to enable low-code and no-code development for novice, intermediate, and experienced users. The company claims this will enable organizations to get new tools, including intelligent, into production faster, while promoting greater collaboration among users to extend and integrate these emerging data capabilities in ways that are both efficient and highly productive. . The company has already aligned its generic platform for specialized use cases in healthcare, supply chain management and other industries.
AI’s contribution to this process is basically the same as in other areas, says Jason Wong of Gartner – that is, taking on routine, repetitive tasks, which in development processes include things like performance testing, QA and data analysis. Wong noted that while the use of AI in no-code and low-code development is still at an early stage, big hitters like Microsoft are very interested in applying it to areas such as platform analytics, data anonymization, and UI development, which the current skills shortage that prevents many initiatives from reaching production-ready status.
Before we start dreaming about an optimized AI-driven development chain, however, we need to solve a few practical problems, the developer said. Anouk Dutree† For starters, abstracting code into composable modules creates a lot of overhead, and this introduces latency to the process. AI is increasingly leaning towards mobile and web applications, where even 100ms delays can drive users away. For back office apps that tend to run quietly for hours on end, this shouldn’t be much of an issue, but it probably isn’t a ripe area for little or no code development either.
In addition, most low-code platforms are not very flexible, as they work with mostly predefined modules. However, AI use cases are usually very specific and dependent on the data available and how it is stored, conditioned and processed. So in all likelihood you’ll need custom code for an AI model to function properly with other elements in the low/no code template, and this could end up costing more than the platform itself. The same dichotomy also affects functions like training and maintenance, where the flexibility of AI clashes with the relative rigidity of low/no code.
However, adding a dose of machine learning to platforms with little and no code can help loosen them up and also add a much-needed dose of ethical behavior. Dattaraj Rao of Persistent Systems recently highlighted how ML can enable users to run pre-canned patterns for processes such as feature engineering, data cleaning, model development, and statistical comparison, all of which should help create models that are transparent, explainable. and be predictable.
It’s probably 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 mitigate their weaknesses in a number of key applications. As the enterprise becomes increasingly dependent on developing new products and services, both technologies can remove the many hurdles that currently stifle this process – and will likely continue to do so whether they work together or independently.
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