Abstract: A method for facilitating automated application performance monitoring threshold management through deep learning model is provided. The method includes retrieving, via an application programming interface, raw data that correspond to an application, the raw data including application performance data; generating data frames based on the raw data, the data frames relating to a multi-dimensional data structure; converting the data frames into a model; developing an error function that optimizes a regression coefficient; training the model by using the error function; and determining, by using the trained model, forecasted threshold values that relate to application performance metrics for the application.

Abstract: Various methods, apparatuses/systems, and media for implementing a smart toolchain integration module are disclosed. A processor detects an issue in connection with performance and/or operation of an application and creates an event, in response to detecting the issue, with required parameters. The processor causes the event to be consumed with the required parameters in an event automation platform and triggers a corresponding microservice which stores a code to be utilized for replicating and interpreting the issue in response to consuming the event. The processor also replicates the issue by running a check through hypertext transfer protocol (HTTP) POST request by the microservice to a corresponding application programming interface (API) based on the code; interprets the issue by sending a HTTP secure (HTTPS) GET request by the microservice to the corresponding API based on the code; and automatically remediates the issue based on the required parameters.

Abstract: Various methods, apparatuses/systems, and media for implementing a smart toolchain integration module are disclosed. A processor detects an issue in connection with performance and/or operation of an application and creates an event, in response to detecting the issue, with required parameters. The processor causes the event to be consumed with the required parameters in an event automation platform and triggers a corresponding microservice which stores a code to be utilized for replicating and interpreting the issue in response to consuming the event. The processor also replicates the issue by running a check through hypertext transfer protocol (HTTP) POST request by the microservice to a corresponding application programming interface (API) based on the code; interprets the issue by sending a HTTP secure (HTTPS) GET request by the microservice to the corresponding API based on the code; and automatically remediates the issue based on the required parameters.