Machine learning should not be treated as a black box

Machine learning in reliability and asset modelling

While Machine learning (ML) algorithms can help us derive insights from data, these insights need to be understood before handing over control to an algorithm. I believe that the best approach is to balance the insights from ML algorithms with engineering principles (reliability engineering in an asset space), validating the analysis of the ML, and linking it back to something explained and understood. If you don’t then:

1. You don’t learn as much,
2. You are exposed to issues / faults in the data,
3. It's harder to correct and improve the algorithms.