Predicting performance of machine learning models

A computer program product for predicting an impact of an adjustment to a machine learning model to key performance indicators, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to:

• receive a proposed new version of a machine learning model;
• calculate, using a regression machine learning model to ingest the proposed new version, a set of value components for a plurality of key performance indicators as indicator values using input data on a specified schedule;
• mapping the calculated indicator values onto scoring payload data;
• calculate a plurality of results for the plurality of key performance indicators, using the set of value components;
• automatically determine whether the plurality of results exceeds a performance threshold;
• recommend the proposed adjustment based on the determination;
• and
• train the regression model by the instructions causing the processor to iteratively perform operations to:
• receive a model scoring payload, an input data set, and a target data set;
• calculate a gradient that is a difference between an input data value of the input data set and a target data value of the target data set;
• and
• propagate the gradient through layers of the regression model to update synaptic weights of the regression model.

A forecasting engine for a machine learning model, comprising:

• one or more processors coupled to one or more memories, the one or more memories comprising:
• an original machine learning model having performance statistics associated therewith;
• performance data for the original machine learning model;
• and
• program instructions that, when executed on the one or more processors, cause the one or more processors to:
• receive a proposed adjustment to the original machine learning model;
• calculate, using a regression machine learning model to ingest the proposed new version, a set of value components for a plurality of key performance indicators as indicator values using input data on a specified schedule;
• mapping the calculated indicator values onto scoring payload data;
• calculate a plurality of results for the plurality of key performance indicators, using the set of value components;
• automatically determine whether the plurality of results exceeds a performance threshold;
• recommend the proposed adjustment based on the determination;
• and
• train the regression model by the instructions causing the processor to iteratively perform operations to:
• receive a model scoring payload, an input data set, and a target data set;
• calculate a gradient that is a difference between an input data value of the input data set and a target data value of the target data set;
• and
• propagate the gradient through layers of the regression model to update synaptic weights of the regression model.