Framework for measuring telemetry data variability for confidence evaluation of a machine learning estimator

A method for generating predictions using a prediction model based on telemetry data from deployments, comprising:

• generating, using the prediction model and second telemetry data obtained from a deployment of the deployments:
• a prediction, and
• a prediction error estimate;
• in response to a determination that the prediction indicates a negative impact on the deployment:
• generating a confidence estimation for the prediction based on a variability of the second telemetry data from the telemetry data;
• in response to a second determination that the confidence estimation indicates that the prediction error estimate is inaccurate:
• remediating the prediction based on the variability to obtain an updated prediction;
• and
• performing an action set, based on the updated prediction, to reduce an impact of the negative impact on the deployment.

A non-transitory computer readable medium comprising computer readable program code, which when executed by a computer processor enables the computer processor to perform a method for generating predictions using a prediction model based on telemetry data from deployments, the method comprising:

• generating, using the prediction model and second telemetry data obtained from a deployment of the deployments:
• a prediction, and
• a prediction error estimate;
• in response to a determination that the prediction indicates a negative impact on the deployment:
• generating a confidence estimation for the prediction based on a variability of the second telemetry data from the telemetry data;
• in response to a second determination that the confidence estimation indicates that the prediction error estimate is inaccurate:
• remediating the prediction based on the variability to obtain an updated prediction;
• and
• performing an action set, based on the updated prediction, to reduce an impact of the negative impact on the deployment.