During our years of work dealing with problems in the industry around Machine Learning, we’ve observed that one of the key moments in a company is the one when it has to define where to invest. For other possible investments, decision makers have usually at least a rough idea of expected returns and impact of investments. That does not always happen in Machine Learning heavy projects due to the intrinsic uncertainty of the research that these projects involve.
In addition to this, even when companies are starting nowadays to work with a paradigm that gives experimentation and validation a central place, we’ve seen that in recent years the hype generated around AI and ML has risen the expectatives around these tools, but at the same time many projects are failing their goals when finding problems not contemplated at the moment of taking a ML centered investment decision. To illustrate and have a deeper analysis on this topic, we are conducting a Survey and we would like to invite you all who are making decisions of investment in innovation / product development / business change / etc to complete it. The survey is here and we’ll be sharing the results once we have them.
P.S.: In case you want to take a sneak peak of how Machinalis handle this problem, you might find interesting this summary.
