No-Code Data Science

The fastest-growing area in programming these days is data analysis. It has become a necessity for businesses of all sizes in order to determine mission critical information such as trends in the marketplace, ways to reduce customer churn, and the ability to pinpoint product line strengths and weaknesses. Until recently only large organizations could afford the services of data analysts, specialists who could construct the artificial intelligence models needed to uncover that information. Now, there are an increasing number of "no-code" platforms that allow anyone to quickly build an accurate machine learning model - without the need for coding or training in data science.

As machine learning models become easier to create, they're also coming into play in areas other than the business world. ML models have been used to make predictions ranging from where and when wildfires are most likely to occur to identifying optimal Covid-19 testing and vaccination strategies. In fact, as time goes on and machine learning (as well as deep learning) models become more sophisticated, no-code AI platforms are bound to be used for data analysis in more and more areas.

Popular no-code machine learning platforms include Teachable Machine (by Google), AutoML (part of Google Cloud), Peltarion, Big ML, Obviously AI, CreateML (for Mac computers), Google ML Kit (to generated models for Android and iOS devices), and MonkeyLearn. Various studies have indicated that platforms like these have the potential to reduce development time for machine learning models by up to 90 percent. They also allow the end users (the people who are the actual decision makers) to construct the exact model they need. 

Along with empowering "citizen developers", no-code AI platforms also provide a couple of other major benefits. They allow experienced data scientists to focus their time and effort on more complex projects which may also lead to more effective no-code solutions. And they make it possible to try out AI solutions much faster and with much less expense involved than with traditional methods. Building models quickly and cheaply can greatly increase the chance of finding a really useful solution to a particular problem, since it's not always clear what type of algorithm would work best.

In some cases a no-code platform will require you to select an algorithm or type of model to fit with your data, select certain training parameters, and then "train" the model on your dataset. Other platforms may simply have you pick one of their pre-trained models and use it to analyze your dataset. In either case the process is done without requiring any coding. Training a model on your own data takes longer, but it can result in a more accurate result. Using a pre-trained model usually works best when you're dealing with a common machine learning problem - like predicting customer churn - where there are standard models available.

The general steps in training a machine learning model include:

• Choosing a dataset from the platform's data store or uploading your own file.
• "Cleaning" the input data - removing any empty or outlier items and possibly "normalizing" the data to get everything on roughly the same scale.
• Choosing a type of algorithm (such as a classification or regression algorithm) to use to train a model on your data. Each algorithm has certain settings that can be adjusted to produce the best fit with your data. The platform may analyze the data and automatically choose what it considers the optimum settings or you may be able to adjust some of the settings yourself.
• Using the majority of the input data to train the model and a small portion for validation to see if the model produces accurate results.
• Deploying the finished model to a website or as part of an app.

That covers the general characteristics of a no-code machine learning platform. In Part 2 of this post I'll go over a few specific examples of creating a machine learning model and what results to expect from that model.