Machine Learning in the Real World - Enterprise Viewpoint

Despite the recent successes in the use of machine learning (ML) to perform tasks more accurately than humans such as in cancer detection and hand-writing recognition, moving beyond a demo to roll out a live commercial product requires much more than a fancy algorithm. There's no doubt that the technology will re-define most industries, but it's worth keeping in mind that we are just at the beginning of a multi-decade cycle and so entrepreneurs should be cognisant of this when implementing ML in a commercial environment with clients. What follows are some lessons learnt from being in the trenches. It's worth clarifying that most problems in ML follow a similar pattern, loosely called "predictive analytics": Building these models requires huge datasets of labeled historical records. For example: loan applications with tags stating whether a loan repayment event occurred in order to make a prediction whether an applicant will repay a loan. The dataset requirement may not sound problematic (even assuming the requisite datasets are available), but the reality is that these datasets are often residing in different areas of the business, in different formats with different labels and with different decision-makers for each dataset.