Learning in the “Real World”

In this paper we define and characterize the process of developing a “real-world” Machine Learning application, with its difficulties and relevant issues, distinguishing it from the popular practice of exploiting ready-to-use data sets. To this aim, we analyze and summarize the lessons learned from applying Machine Learning techniques to a variety of problems. We believe that these lessons, though primarily based on our personal experience, can be generalized to a wider range of situations and are supported by the reported experiences of other researchers.