Not long ago, working in computing required extensive expertise. If you wanted to create a program or an application you...
Not long ago, working in computing required extensive expertise. If you wanted to create a program or an application yourself, it was imperative that you could code. However, as the age of computing has continued to evolve, so has access to this ability. Today, in the age of artificial intelligence, we have low-code and no-code options and a great many ways you can utilise them.
Complex programs and applications still require coding, but low-code and no-code systems allow users to:
Coding workstation by Farzad
As the name suggests, low-code refers to systems that require less coding, while no-code are systems without the need for code at all. Both approaches are popular and targeted towards professionals and business people. Furthermore, they are used in artificial intelligence and machine learning models, outside of regular computing.
Additionally, both approaches are typically used to serve specific business purposes, like data classification, or the definition of a workflow. Low-code goes hand in hand with no-code, but these platforms are also frequently used by developers themselves. Often, these experienced programmers take advantage of these tools to make their jobs easier, by avoiding writing extra code. While no-code is most often used by managers in different industries and departments.
By Piret Ilver
Development is simplified. Without the need to write code, users can quickly learn how to bring their creations to life. As such, it gives the users more time to actually focus on determining what they want their development or algorithm to do.
Programming is a meticulous process that requires attention and persistence to achieve good results. However, using platforms that allow for low-code and no-code development means that users can easily switch around pre-made components. This means that users can go through the process of trial and error more quickly, which in turn speeds up overall development.
For the bottom line of any professional or business, the reduction of cost in any way can be truly beneficial. Using a no-code or low-code system for development can reduce time requirements and the necessity of heavy maintenance. Moreover, it can allow businesses to try out new ideas inexpensively, thereby increasing productivity.
Given that the program or algorithm isn’t built by an outside AI engineer or consultant, the data is handled by the professional or business itself. In this case, any sensitive data is kept within the walls of the ultimate user. Following on from this, using no-code and low-code platforms is secure as it keeps third-party developers out.
However, there is the issue of platform security. Some platforms may fail to design secure access protocols. Therefore this can mean that users should take care to research the terms and conditions.
Despite the speed of low-code and no-code platforms, their use cases and functionalities are often limited. Given that most platforms are designed to address specific problems, it’s difficult to use them for creating more complex solutions.
Even if the creation of models, algorithms, or applications is made easy by low-code and no-code AI, the deployment still requires a certain degree of understanding. Such understanding needs to be taught to the management team, or the people that will mainly use the finished product.
By Joshua Golde
A Google-backed platform that allows developers with limited ML expertise to train high-quality models specific to their business needs. The platform focuses on image and video annotation and labelling. As well as this, it also has the ability to complete semantic text analysis and classification.
An Apple-backed no-code platform that uses a Mac OS framework. It allows users to easily build ML models with an easy-to-use app interface and no code. The platform can train a variety of models, from image recognition to sentiment and regression analysis.
A no-code platform that allows users to train and build AI models. The platform focuses on image, text, and document classification. It enables users to train custom models on their use-case-specific data.
Custom models also have a human-in-the-loop option, which means the model asks for input where it is unsure. Some use cases are automated data iteration, and classification of images, texts, and documents.
A no-code AI platform that builds ML algorithms for data prediction. Users can take a bird’s-eye view of existing data, understand it and draw conclusions.
The platform also suggests ready-made datasets, so you can test them out and get predictions right away. Some business-case usage would be personalisation of marketing campaigns, forecasting of company revenue, and supply chain optimisation.
A leading platform designed to build the highest quality training datasets available for computer vision and natural language processing. This platform has advanced tools, like automation features, data curation, offline access, and integrated annotation services. It allows ML teams to build incredibly accurate datasets 3-5x faster.
In the end, low-code and no-code AI solutions both have their advantages and disadvantages alike. These accessible platforms are removing barriers and opening up AI to a broader audience without the need for a computer science or engineering degree.
Without requiring in-depth coding experience, they enable people from a variety of backgrounds to develop and use AI solutions because of their user-friendly interfaces and simpler processes. With the potential to swiftly launch AI apps, optimise operations, and encourage creativity, this is significant for both individuals and businesses.
The adoption of low-code and no-code platforms goes beyond simply convenience; they represent an important shift in the way we approach creativity and problem-solving in the digital era. They encourage a more inclusive environment where ideas may be implemented with fewer barriers to technology.
The growth of AI applications may be assisted by small businesses, entrepreneurs, and even enthusiasts because of the liberalisation of AI technology. These platforms have the potential to significantly contribute to the development of AI and further its incorporation into both our personal and professional life in the future.
This industry has huge potential for growth and innovation, with major breakthroughs predicted throughout a range of industries, such as banking and healthcare. As well as this, the use of low-code and no-code AI solutions could be the solution to some of your professional or business problems.
If it is the case that you or your business requires a more tailored solution for a more complex problem, then contact us here at Gemmo AI.
Author: Michelle Diaz