The present disclosure relates to system(s) and method(s) to generate alerts while monitoring a machine learning model in real time. The system is configured to receive, in response to a first input parameter, a first output parameter generated by a first function of a learning model corresponding to a machine learning model. The system is further configured to receive, in response to a second input parameter, a second output parameter generated by a second functionality of a real-time model corresponding to the machine learning model. Further, the system is configured to compare the first output parameter with the second output parameter and the first input parameter with the second input parameter to generate tuning and rebuilding alerts.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method to generate alerts while monitoring a machine learning model in real time, the method comprising steps of: receiving, by a processor, in response to a first input parameter, a first output parameter generated by a first function of a learning model corresponding to a machine learning model; receiving, by a processor, in response to a second input parameter, a second output parameter generated by a second functionality of a real-time model corresponding to the machine learning model, wherein the first functionality and the second functionality are configured to perform a same function; comparing, by the processor, the first output parameter with the second output parameter to determine an output variation score, wherein the comparison is based on a data type corresponding to the first output parameter and the second output parameter; comparing, by the processor, the first input parameter with the second input parameter to determine an input variation score, wherein the comparison is based on a data type corresponding to the first input parameter and the second input parameter; generating, by the processor, a tuning alert to tune the machine learning model, when the output variation score is above a first threshold value; and generating, by the processor, a model rebuilding alert to rebuild the machine learning model, when the input variation score is above a second threshold value. 2. The method of claim 1, wherein the data type associated with the output parameters and the input parameters is selected from numeric, categorical, and text. 3. The method of claim 2, wherein the comparison of the output parameters and the input parameters is based on a statistical property validation technique when the data type corresponding to the output parameters and the input parameters is numeric, wherein the comparison of the output parameters and the input parameters is based on a label correction technique when the data type corresponding to the output parameters and the input parameters is categorical, and wherein the comparison of the output parameters and the input parameters is based on a standard text processing when the data type corresponding to the output parameters and the input parameters is text. 4. The method of claim 1, wherein the first threshold value and the second threshold value is determined based on the data type associated with the output parameters and the input parameters. 5. A system to generate alerts while monitoring a machine learning model in real time, the system comprising: a memory; a processor coupled to the memory, wherein the processor is configured to execute programmed instructions stored in the memory for: receiving, in response to a first input parameter, a first output parameter generated by a first function of a learning model corresponding to a machine learning model; receiving, in response to a second input parameter, a second output parameter generated by a second functionality of a real-time model corresponding to the machine learning model, wherein the first functionality and the second functionality are configured to perform a same function; comparing the first output parameter with the second output parameter to determine an output variation score, wherein the comparison is based on a data type corresponding to the first output parameter and the second output parameter; comparing the first input parameter with the second input parameter to determine an input variation score, wherein the comparison is based on a data type corresponding to the first input parameter and the second input parameter; generating a tuning alert to tune the machine learning model, when the output variation score is above a first threshold value; and generating a model rebuilding alert to rebuild the machine learning model, when the input variation score is above a second threshold value. 6. The system of claim 5, wherein the data type associated with the output parameters and the input parameters is selected from numeric, categorical, and text. 7. The system of claim 6, wherein the comparison of the output parameters and the input parameters is based on a statistical property validation technique when the data type corresponding to the output parameters and the input parameters is numeric, wherein the comparison of the output parameters and the input parameters is based on a label correction technique when the data type corresponding to the output parameters and the input parameters is categorical, and wherein the comparison of the output parameters and the input parameters is based on a standard text processing when the data type corresponding to the output parameters and the input parameters is text. 8. The system of claim 5, wherein the first threshold value and the second threshold value is determined based on the data type associated with the output parameters and the input parameters. 9. A non-transitory computer readable medium embodying a program executable in a computing device to generate alerts while monitoring a machine learning model in real time, the computer program product comprising: a program code for receiving, in response to a first input parameter, a first output parameter generated by a first function of a learning model corresponding to a machine learning model; a program code for receiving, in response to a second input parameter, a second output parameter generated by a second functionality of a real-time model corresponding to the machine learning model, wherein the first functionality and the second functionality are configured to perform a same function; a program code for comparing, the first output parameter with the second output parameter to determine an output variation score, wherein the comparison is based on a data type corresponding to the first output parameter and the second output parameter; a program code for comparing, the first input parameter with the second input parameter to determine an input variation score, wherein the comparison is based on a data type corresponding to the first input parameter and the second input parameter; a program code for generating, a tuning alert to tune the machine learning model, when the output variation score is above a first threshold value; and a program code for generating, a model rebuilding alert to rebuild the machine learning model, when the input variation score is above a second threshold value.