IDP: Machine Learning and the Training Required for Reliable Results

In a previous article, Part 1, I focused on whether there is an ability to enjoy the strengths of machine learning without the requirement to train it. The underlying rationale for this question is simple: while machine learning can greatly reduce traditional IDP configuration efforts, we exchange the efforts associated with manual configurations for efforts to compile and tag reliable training data. Is there a pure benefit of moving in this direction of emphasis on training data? Even with manual configuration, the best practice is to perform and test this work on the same type of data. Compiling data is a much simpler and less expensive cost (easily 1/10th of the cost).