For commercial success of machine learning models, it is important to understand that the commercial applications require the highest degree of functionality and reliability. The success metric is mostly binary, as compared to the incrementally improving statistical metrics. This calls for a high level of explainability and transparency about how the ML model functions. This toolkit explains both the analytics and operational framework to address these needs.
