Methods, computer program products, and systems can include obtaining a first computing environment specific application deployment software code instance associated to a first computing environment, the first computing environment specific application deployment software code instance for deployment of a certain application on the first computing environment; parsing the first computing environment specific application deployment software code instance, wherein the parsing includes determining attributes of the first computing environment specific application deployment software code instance and generating, using the determined attributes of the first computing environment specific application deployment software code instance, a computing environment agnostic semantic tree data structure that expresses a workflow pattern for deployment of the certain application; and composing, with use of the computing environment agnostic semantic tree data structure, a second computing environment specific application deployment software code instance associated to a second computing environment for deployment of the certain application on the second computing environment.

Methods, computer program products, and systems can include obtaining a first computing environment specific application deployment software code instance associated to a first computing environment, the first computing environment specific application deployment software code instance for deployment of a certain application on the first computing environment; parsing the first computing environment specific application deployment software code instance, wherein the parsing includes determining attributes of the first computing environment specific application deployment software code instance and generating, using the determined attributes of the first computing environment specific application deployment software code instance, a computing environment agnostic semantic tree data structure that expresses a workflow pattern for deployment of the certain application; and composing, with use of the computing environment agnostic semantic tree data structure, a second computing environment specific application deployment software code instance associated to a second computing environment for deployment of the certain application on the second computing environment.

An approach is provided in which the approach loads a machine learning model and a set of test case statistical data into a user system. The set of test case statistical data is based on a set of test cases corresponding to the machine learning model and includes a plurality of input parameter sets and a corresponding set of output quality measurements. The approach compares user data on the user system against the set of test case statistical data and identifies one of the plurality of input parameter sets to optimize the machine learning model based on the set of output quality measurements. The approach generates an optimized machine learning model using the machine learning model and the identified input parameter set at the user system.

An approach is provided in which the approach loads a machine learning model and a set of test case statistical data into a user system. The set of test case statistical data is based on a set of test cases corresponding to the machine learning model and includes a plurality of input parameter sets and a corresponding set of output quality measurements. The approach compares user data on the user system against the set of test case statistical data and identifies one of the plurality of input parameter sets to optimize the machine learning model based on the set of output quality measurements. The approach generates an optimized machine learning model using the machine learning model and the identified input parameter set at the user system.

In some examples, a system receives first measurements of data items used by a build server in building an executable program, the data items copied from a data repository to a storage partition that is separate from the data repository, and the storage partition to store the data items relating to building the executable program by the build server. The system determines, based on the first measurements and according to a policy specified for the storage partition, whether a corruption of the data items used by the build server in building the executable program has occurred.

In a computer system, an orchestration platform includes extensible components that interact with external systems and technology. The platform extension deploys a surrogate component or probe that acts as a bridge between the core platform and the extension technology.

There are provided systems and methods for a compute platform for machine leaning model roll-out. A service provider, such as an electronic transaction processor for digital transactions, may provide intelligent decision-making through decision services that execute machine learning models. When deploying or updating machine learning models in these engines and decision services, a model package may include multiple models, each of which may have an execution graph required for model execution. When models are tested from proper execution, the models may have non-performant compute items, such as model variables, that lead to improper execution and/or decision-making. A model deployer may determine and flag these compute items as non-performant and may cause these compute items to be skipped or excluded from execution. Further, the model deployer may utilize a pre-production computing environment to generate the execution graphs for the models prior to deployment or upgrading.

Various techniques for emulating edge locations in cloud-based networks are described. An example method includes generating an emulated edge location in a region. The emulated edge location can include one or more first computing resources in the region. A host in the region may launch a virtualized resource a portion of the one or more first computing resources. Output data that was output by the virtualized resource in response to input data can be received and reported to a user device, which may provide a request to migrate the virtualized resource to a non-emulated edge location. The non-emulated edge location may include one or more second computing resources that are connected to the region by an intermediary network. The virtualized resource can be migrated from the first computing resources to at least one second computing resource in the non-emulated edge location.

Various techniques for emulating edge locations in cloud-based networks are described. An example method includes generating an emulated edge location in a region. The emulated edge location can include one or more first computing resources in the region. A host in the region may launch a virtualized resource a portion of the one or more first computing resources. Output data that was output by the virtualized resource in response to input data can be received and reported to a user device, which may provide a request to migrate the virtualized resource to a non-emulated edge location. The non-emulated edge location may include one or more second computing resources that are connected to the region by an intermediary network. The virtualized resource can be migrated from the first computing resources to at least one second computing resource in the non-emulated edge location.

There are provided a method of managing radio connectivity of a vehicle and a system thereof. The method comprises: continuously receiving by vehicle's telematic system a predictive model generated by remote system using data continuously collected from a plurality of vehicles, wherein the collected data comprise, for each given vehicle of the plurality of vehicles, data informative of its location, speed and of Radio Access Technology (RAT)-related measurements; and, responsive to a predefined event, applying, by the telematic system, a lastly received predictive model to current values of a predefined set of inputs associated with the vehicle to obtain instructions and respectively provide corrective actions related to radio connectivity of the vehicle. The corrective actions include modifying RRC measurement report(s) so as to force the cellular network to provide one of: intra-RAT handover, inter-RAT handover; excluding available connectivity with undesired RAT or band; and terminating the radio connectivity with further RAT re-selecting.