Intuit, Inc.

Manas Kumar Mukherjee

Efraim David Feinstein

Sumanth Venkatasubbaiah

Ace Vu

Jarred I Keneally

Vishal Aggarwal

Prabhasana Vanka

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.

Schema-based machine learning model monitoring

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. The method also includes generating the AQI based on the weighted value of the two or more sub-indexes, sending the generated AQI to the SAAS multi-tenant application, and receiving an update to the SAAS multi-tenant application based on the generated AQI.

Systems and methods for determining an application quality index

Certain aspects of the present disclosure provide techniques for providing a graphical user interface to assist a user of an application programming interface (API). Embodiments include determining that the user has selected an attribute of the API within an API explorer. The API explorer may be displayed with a developer interface of a web application. Embodiments further include identifying a user interface component of the web application that is related to the attribute of the API. Embodiments further include displaying an indication within the developer interface that the user interface component is related to the attribute of the API.

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Mountain View, CA, United States of America

Manas Kumar Mukherjee