Machine Learning has wholly integrated into our everyday life. Naturally, many aspiring would-be analysts want to learn the magic behind ML. However, the application of the machine learning process in analyzing data involves a framework of collecting data, creating/choosing a model, preparing, and training the data until predicting the outcome. All of which makes the entire process appear too technical, challenging, and laborious, leaving those who aren't fluent in software development feeling overwhelmed. However, No-code Machine Learning is changing this status quo.
Only 25% of organizations are using artificial intelligence (AI) in their businesses today. Why? Custom AI-enabled solutions are expensive to build, as talented data scientists are a hot commodity today and don't come cheap. Top performers can easily command over $250,000 in annual salary, which seriously makes us question the money we wasted invested in getting our MBAs. Not to mention, it can take months or even years to implement. CTOs are understandably suspicious of the latest buzzword du jour, so you need to show results fast.
What are the implications of the growing low-code/no-code movement to professional developers and their business counterparts? Some industry experts argue that the time has come for business users to be able to steer their own destinies when it comes to application development. That's the message conveyed at a recent conference focused on this very topic, sponsored and hosted by Ninox. The Covid-19 crisis illustrated the advantages low-code and no-code are bringing to the world. "Some IT organizations are faring better if they already have low-code platforms in their tool belts," according to John Bratincevic, analyst with Forrester.
AI applications have made their way into almost every sector, yet; businesses fail to adopt them. According to Forbes, 83 percent of companies think AI is a strategic priority for them, yet there is a shortage of skilled data scientists. This is not only because AI solutions and expertise are expensive but also because firms lack the infrastructure to support these solutions. Companies are increasingly deploying AI and machine learning models using no-code AI, a no-code development platform with a visual, code-free, and typically drag-and-drop interface. Non-technical people may quickly classify, evaluate, and develop accurate models to make predictions with no coding AI.
Artificial intelligence and machine learning today allow us to reduce the time demanded for solving a certain problem from months and years to minutes. For example, it took your grandma years of practice to learn the right temperature for growing her zucchinis. If she had AI, she would do it much faster. But the problem is that even today not so many people can program ― only 0,5%! This is where no-code platforms step into the game.