Abstract: The present disclosure provides techniques for schema-based machine learning model monitoring. One example method includes receiving input data to and output data related to a machine learning model, wherein the input data and the output data conform to a data schema, retrieving, based on the data schema, a set of fields associated with the input data and the output data, performing statistical analysis for the machine learning model based on the set of fields retrieved, and predicting one or more attributes of the machine learning model based on the statistical analysis, wherein the one or more attributes of the machine learning model indicate a result of monitoring of the machine learning model, explainability information related to the machine learning model, or health of the machine learning model.

Abstract: A computer-implemented method for determining an Application Quality Index (AQI) Score of a software-as-a-service (SAAS) multi-tenant application to a SAAS system is provided. The method may be performed on a computing device executing a processor. The method may include accessing data at a data repository. The accessed data includes at least connection/disconnection data and API error data of the SAAS multi-tenant application. The method also includes processing the accessed data to generate two or more sub-indexes. The two or more sub-indexes includes at least a first sub-index based on the connection/disconnection data and a second sub-index based on the API error data. The first sub-index is assigned a weighted value based on content of the connection/disconnection data and the second sub-index is assigned a weighted value based on content of the API error data.