Over the past years, digitalization has been a buzzword for many companies, as they try to take advantage of new technologies, including Artificial Intelligence (AI) and cloud environments, to improve operations, centralize data, and enhance customer engagement.
In every pandemic crisis throughout the history, analyzing data and numbers mattered. And the history isn’t short. In modern days, collected information material on crisis was analyzed upon the end of each pandemic, resulting in statistic reports, based on which key takeaways were drawn out. This knowledge accumulation is invaluable, and represents the foundation of modern epidemiology, setting up the rules and guidelines for handling pandemic crisis situations. On the other hand, there’s no doubt that AI provides an abundant arsenal of tools used to fight pandemic.
When you read about Machine Learning (ML), and what this concept represents, everything may seem idyllic and simple - you have some data, you pass it on to an ML algorithm, and voila, magic happens. You get a model that can predict the future. Not long ago, I would’ve said: "Not so fast, there is a LOT of manual work behind this story, and you can't simplify ML just like that." However, today, when Automated Machine Learning (AutoML) is a reality, I would’ve to think twice before saying this, and maybe even believe that magic does exist.