AI is no more confined within the walls of research labs, is transforming the pace of businesses across sectors and could potentially deliver an additional economic output of around $13 trillion by 2030. To make this prediction come true, the industry needs to make AI work for them, but as per a Deloitte survey, at least 40 per cent of businesses believe AI technologies and expertise are too expensive. 

Secondly, AI is a narrow high-tech niche of programming that requires in-depth knowledge of coding. For smaller companies, keeping their resources in mind, it’s too difficult for them to build and maintain a team of professionals with advanced tech skills for AI applications. This is where low-code/no-code AI comes into play. 

“Low code and no-code platforms such as AutoViML are the waves of the future. There is no getting away from them whether you are in a big enterprise or in a small startup. The reasons are that they provide an attractive combination of high productivity and fast experimentation which would be difficult to do if you were to code for every scenario yourself in modelling. However, as we know in-car driving, driving fast is pleasurable up to a point. Beyond a point, driving faster is dangerous. Similarly, though these tools may let you do a tremendous amount of experimentation, you must have clear goals (and/or timelines) in mind before starting with them. That way, you can stop at some point and get the most value,” said Ram Seshadri, Google Machine Learning Program Manager. 

But what is no-code and low-code AI?

No-code AI is nothing but a subset of AI is a code-free system, and frequently drag-and-drop interface to deploy AI and machine learning models. It makes data classification and analysis easier for AI models with specific business applications. Moreover, it enables people from a non-tech background i.e., without prior exposure to coding, thereby democratising AI. During the pandemic, when businesses had to shift their operations online realised that they don’t have the time and resources to develop their own applications. This is when no-code became more popular. 

On the other hand, low-code AI development platforms are ready-made building blocks that can help create AI solutions, with the users crafting applications in no time with the help of GUIs and configuration instead of hand-written code. However, low-code, unlike no-code requires a minimal coding language. 

Advantages attached but caution required 

Businesses ought to benefit more in data-driven sectors including sales, finance, and marketing with the help of no-code/no-code AI platforms as they have numerous advantages attached. It includes: 

• Low-code/no-code platforms allow non-technical people or enterprises to create AI systems from the ground up, making AI more accessible to a broader array of sectors. 
• These tools frequently offer an intuitive drag-and-drop interface with minimal complexity, making it relatively simple to navigate through low-code/no-code AI platforms. 
• As low-code/no-code AI platforms frequently include pre-built AI models, project templates, and ready-to-use datasets, labelling and iterating the data takes much less time, significantly speeding up model development. 
• AI automates work for dozens (if not hundreds) of users, saving the firm time and resources. Furthermore, the servers are automatically scaled up or down based on the load, and the load and progress are both very easy to track. 

Till now, the use of low-code/no-code AI platforms seem encouraging but certain drawbacks need to be looked upon. First, there is a need to understand the terms and conditions to know how and where the data will be stored is a must. Second, low-code/no-code platforms are typically limited in functionality, owing to the fact that they're created to solve a specific problem, and it's difficult to come up with out-of-the-box, more complicated solutions. Third, the ML engineer, HR professional, and even an intern should all be able to use the low-code/no-code platforms equally, but this isn't always the case. Anyways, the end-user of an AI platform is an ML engineer, the rest of the team will need a lot of training and consultations to get their heads around AI procedures. 

Even though low-code/no-code platforms are selling hot in the market, still, a lot of areas and use cases are yet to be explored when it comes to machine learning, artificial intelligence, and computer vision. Thus, the custom AI model-building approach isn't going away anytime soon. Furthermore, low-code and no-code platforms are constrained in terms of feature modification, whereas when designing AI from the scratch, the sky is the limit. With this, one is free to create the architecture, functionality, and pipeline that best suits the project’s requirements.

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