I’ve had good success using decorators and infrequently also metaclasses to cleanly solve this type of thing in Python. However I somewhat disagree with the ar

I’ve had good success using decorators and infrequently also metaclasses to cleanly solve this type of thing in Python. However I somewhat disagree with the article in the sense that instrumenting reporting logic is just table stakes bare minimum of software in a business. 90% of business software is about creating a report for somebody somewhere, often needing to be rapidly changed for ad hoc requests from product or management people. Reporting code is the code. It may even be the business logic in a truer sense than the application logic itself. Another lesson along these lines applies to machine learning and modeling systems: the mathematical algorithm is always the easiest part. The hard part is situation-by-situation customized data ingestion (things that cannot be solved by standardizing on platform-specific data formatting, like Hadoop or Spark), and then also defining business metrics that capture the success of the model at a high level (as opposed to engineering metrics like accuracy, precision and recall, etc). Model training and the 10% of the project spent tweaking an algorithm or optimizing things just pales in comparison to the type of systems you need to facilitate efficient custom data ingestion that can be arbitrarily different on a per project basis.