Hi-tech entrepreneurs and academics are concerned with where and how much investment they require to commercialize their lab technology while investors want to know “is that technology good to invest in?” If you have a good technology or idea and thinking of commercializing it, consider this text as a road map.

For the past 25 years, I’ve seen thousands of times when a person makes errors — but never when a machine makes a mistake. Today, a blunder in the learning projects can cost companies millions and several years of useless work. For this reason, the most common errors in machine learning related to data, metrics, validation, and technology are collected here.

Modern deep-tech start-ups are not just guys making a piece of hardware in a garage for fun. These are advanced, cohesive teams of like-minded people, who are building market-ready products to solve specific customer problems. They’re using dedicated hardware designs with core technologies (IP) transferred from research labs and seamlessly integrated with cloud software.