Data science projects can be complex, and the complexity only increases when it comes to operationalizing the results. It is no easy task to maintain and manage a large codebase with millions of lines of code for data science or machine learning projects. No-code/low-code data science solutions, on the other hand, solve this problem by providing a simplified approach to building and deploying data science projects.
No Code/Low Code Solutions are Democratizing Data Science
No-code data science functionalities are democratizing data science by making it more accessible to non-technical users. By providing a GUI, these platforms make it possible for anyone to build and deploy machine learning models, regardless of their coding skills. These solutions simplify the process and allow users to generate workflows and models using natural language descriptions.
Bigger Picture Progress
In the early days, companies like IBM were creating no-code solutions, although they may not have been as powerful as they are today. This is not a new concept, and it is important to acknowledge that it has been around for some time.
It is a well-proven and matured way of solving problems. You can do everything from traditional classification and regression to time series forecasting, image and video analysis. This wide range of capabilities is a result of the maturity and longevity of the field.
"One exciting development is the use of foundation models, large language models that can be used for authoring," commented Ingo Mierswa, SVP of Product Development at Altair and founder of RapidMiner, a data science platform. "We have been exploring new authoring modalities, such as using natural language to describe the problem and solution, and having the platform automatically generate the necessary workflows and models." This further accelerates and scales the use of data science, empowering even more people to do it the right way. "We are integrating this new modality with the existing ones, so users can customize the results using workflows or code. And when we showcased our first iteration of this at a conference, people were very excited about it because it makes data science easier than ever," he remarked.
No-code data science is going to be the future for pretty much 99% of data science projects. "I'm not saying that learning to code is a waste of time, but it doesn't teach you the right concepts," Mierswa suggests. "You get so caught up in syntax and programming languages that you lose track of what really matters in data science. The concepts are timeless, but the problems are not. Throughout my career, I've seen multiple programming languages come and go. Python has been around for 30 years, but in a couple of years, nobody may ask for it again." Learning the concepts will pay off in the long run, he stressed, while learning a programming language only provides short-term benefits.
No-code data science solutions make it easier to understand the concepts without getting bogged down by syntax and programming. "You can focus on what really happens on a higher level and have a better understanding of the underlying principles," Mierswa highlights. "At RapidMiner, we offer two things that help with this: our self-paced portal called the RapidMiner Academy, where you can get certified and learn about data science concepts without writing a single line of code, and our Center of Excellence approach, which guides you through the process of doing data science work without actually doing it for you. It's like a driver's ed approach, where we sit in the passenger seat and give you helpful hints."
The Way Forward
Consider computer scientists who have learned to program and code in their studies; they would be the first ones to admit it. The interesting thing about data science is that there are so many people coming from adjacent fields like engineers and statisticians who are not computer scientists. For many of them, coding suddenly looks so powerful. They realize they can create whatever solution they want, which is part of the appeal for many computer scientists in the first place. But if you're still solving the same problem for the second time and still coding, you did it wrong the first time. Every computer scientist knows this because they are trained in this school of thought. However, if you're from adjacent areas and didn't go through all the studying in college, this may not come naturally to you. It may be exciting to change a line of code and see the computer do things, but it gets old quickly and becomes a waste of resources.
Companies will have to question if this waste is sustainable in the future. There will always be a place for coding when solving a problem for the first time, but in all other cases, it's a waste of time and resources.