Low Code Data Science is the Future
AI — or Data Science if you prefer — has become mainstream. It is not a research niche anymore. Random Forests and Deep Learning networks are now available in libraries and tools, waiting to be applied to business problems and real-world data. This is so true that the focus of AI has now shifted from proposing new paradigms and new algorithms to engineering existing solutions via a standardized sequence of MLOps. When something moves away from research and becomes mainstream, other segments of the data analytics society claim access to it.
Can people who haven’t learned how to code, like marketing analysts, physicians, nurses, CFOs, accountants, mechanical engineers, auditing professionals, and many other professional figures, successfully implement AI solutions?
A friend of mine just started out as a marketing analyst. She has a statistics background, and no coding experience. Her first task was to implement a solution for sentiment analysis. As we all know, many options are possible here. You can go for an external service, you can implement a dictionary-based grammar-inclusive solution, or you can train a machine learning / deep learning model to distinguish positive from negative sentences.
External services are efficient and most of them are ready to go, but they have a price tag that does not really become cheaper with extensive usage. A dictionary-based solution seems easy at first, but it is actually quite complex to implement, because of the many grammar rules required to reach adequate performances. Eventually, she leaned towards the machine learning approach.
She quickly learned how to assemble data operations within KNIME Analytics Platform, which is open source and free. Then, she located two useful no-code workflows on the KNIME Hub: one using a decision tree (workflow: Sentiment Analysis using a Decision Tree) and one using a deep learning neural architecture (workflow: Sentiment Analysis using Deep Learning). Being somewhat more familiar with the decision tree algorithm, she downloaded that workflow, imported it into her KNIME Analytics Platform installation, and customized it to fit her own data.
The whole implementation took a few days: a worthy investment for a successful solution.
The second story is from an employee working in the anti-fraud department at a large bank. He knows about the potential of AI into fraud prevention, he has read tons about it. A promising approach, in case of missing fraud data, is offered by the neural autoencoder.
He has some coding skills, learned in college and in his first jobs. He can assemble a few lines of Python code, if needed, but cannot — and does not have the time to — really code a fully working reliable and robust data science application. All coders were absorbed by other, as usual more important, projects. So, he opted for a low code approach.
