Abstract: A method for dynamically adjusting a proxy server timeout includes identifying each microservice within a proxy server and mapping a topology of microservices, assigning each microservice to one of an application layer, a middleware layer and an infrastructure layer. Defining each representational state transfer (REST) application programming interface (API) calling relationship between each microservice and each other microservice. Determining a corresponding regression model defining a response time of each microservice based at least in part on a set of available response time predictors. Building a sequence model for at least one microservice in the application layer. Predicting an incoming REST API call and identifying a probable sequence model corresponding to the predicted incoming REST API call. Updating a timeout value of the predicted REST API call within the proxy server based on the sequentially predicted response times.

Abstract: An embodiment for an improved method of automated version management for machine learning pipeline development is provided. The embodiment may compute a quality value of a target machine learning pipeline at a predetermined regular interval and automatically save updated versions of the target machine learning pipeline. The embodiment may extract a series of key features from detected versions of the target machine learning pipeline and cluster the detected versions of the target machine learning model pipeline by the extracted series of key features. The embodiment may identify a highest-quality version within each of a series of generated clusters. The embodiment may compute similarity scores for subsets of versions within each of the series of generated clusters. The embodiment may generate and output to a user, a visual representation of the series of generated clusters including version quality data and version similarity data.

Abstract: Computer-implemented methods for optimizing the structure of JSON data are provided. Aspects include receiving first JavaScript Object Notation (JSON) data, where the first JSON data includes a plurality of data objects. Aspects also include generating, based on the first JSON data, second JSON data. The second JSON data includes one or more flattened data objects, where the one or more flattened data objects correspond to one or more first data objects of the first JSON data and are absent a hierarchical structure. The second JSON data includes one or more non-flattened data objects, where the one or more non-flattened data objects correspond to one or more second data objects of the first JSON data.

Abstract: Computer-implemented methods for optimizing the structure of JSON data are provided. Aspects include receiving first JavaScript Object Notation (JSON) data, where the first JSON data includes a plurality of data objects. Aspects also include generating, based on the first JSON data, second JSON data. The second JSON data includes one or more flattened data objects, where the one or more flattened data objects correspond to one or more first data objects of the first JSON data and are absent a hierarchical structure. The second JSON data includes one or more non-flattened data objects, where the one or more non-flattened data objects correspond to one or more second data objects of the first JSON data.

Abstract: A method for dynamically adjusting a proxy server timeout includes identifying each microservice within a proxy server and mapping a topology of microservices, assigning each microservice to one of an application layer, a middleware layer and an infrastructure layer. Defining each representational state transfer (REST) application programming interface (API) calling relationship between each microservice and each other microservice. Determining a corresponding regression model defining a response time of each microservice based at least in part on a set of available response time predictors. Building a sequence model for at least one microservice in the application layer. Predicting an incoming REST API call and identifying a probable sequence model corresponding to the predicted incoming REST API call. Updating a timeout value of the predicted REST API call within the proxy server based on the sequentially predicted response times.

Abstract: An embodiment for an improved method of automated version management for machine learning pipeline development is provided. The embodiment may compute a quality value of a target machine learning pipeline at a predetermined regular interval and automatically save updated versions of the target machine learning pipeline. The embodiment may extract a series of key features from detected versions of the target machine learning pipeline and cluster the detected versions of the target machine learning model pipeline by the extracted series of key features. The embodiment may identify a highest-quality version within each of a series of generated clusters. The embodiment may compute similarity scores for subsets of versions within each of the series of generated clusters. The embodiment may generate and output to a user, a visual representation of the series of generated clusters including version quality data and version similarity data.