Predicting performance of machine learning models

1. A computer-implemented method for predicting an impact of an adjustment to a machine learning model to key performance indicators, the method comprising:

receiving a proposed adjustment to a machine learning model;

calculating, using a regression machine learning model to ingest the proposed adjustment, a set of value components for a key performance indicator (KPI) as indicator values using input data on a specified schedule;

mapping the calculated indicator values onto scoring payload data;

calculating a plurality of results for the KPI using the set of value components;

automatically determining whether the plurality of results exceeds a performance threshold;

recommending the proposed adjustment based on the determination; and

training the regression model by iteratively performing:

receiving a model scoring payload, an input data set, and a target data set;

calculating 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

propagating the gradient through layers of the regression model to update synaptic weights of the regression model.

2. The computer-implemented method of claim 1 , further comprising analyzing a set of machine learning model metrics against a plurality of key performance indicators (KPIs).

3. The computer-implemented method of claim 1 , further comprising calculating metric values for the proposed adjustment using model scoring payload data slices.

4. The computer-implemented method of claim 1 , wherein determining whether the plurality of results exceeds the performance threshold comprises:

deploying the proposed adjustment in a test environment;

defining an expected model quality parameter; and

monitoring the proposed adjustment in the test environment to produce test data; and

recommending the proposed adjustment to a user when the test data exceeds the expected model quality parameter.

5. The computer-implemented method of claim 4 , further comprising approving the proposed adjustment based on the recommendation.

6. 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.

7. The computer program product of claim 6 , further comprising program instructions to analyze a set of machine learning model metrics against the plurality of key performance indicators.

8. The computer program product of claim 6 , further comprising program instructions to calculate metric values for the proposed new version using model scoring payload data slices.

9. The computer program product of claim 6 , wherein determining whether the plurality of results exceeds the performance threshold comprises:

deploying the proposed new version in a test environment;

defining an expected model quality parameter; and

monitoring the proposed new version in the test environment to produce test data; and

recommending the proposed new version to a user when the test data exceeds the expected model quality parameter.

10. 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.

11. The forecasting engine of claim 10 , further comprising program instructions executable by a processor to cause the processor to analyze a set of machine learning model metrics against a plurality of key performance indicators.

12. The forecasting engine of claim 10 , further comprising program instructions to calculate metric values for the proposed adjustment using model scoring payload data slices.

13. The forecasting engine of claim 10 , further comprising program instructions to automatically deploy the proposed adjustment based on the determining.