Abstract: Systems, computer-readable media, and methods are disclosed for generating machine learning model packages by training a machine leaning model, obtaining metadata corresponding to the model, generating a model execution script for executing the model, obtaining a re-training program associated with the model, where the re-training program can be used to re-train the model in a run-time environment of a target application, and generating a machine learning model package that includes the model, the metadata, the model execution script, and the re-training program. Systems, computer-readable media, and methods are also disclosed for deploying, discovering, and managing models by obtaining a machine learning model package, obtaining a model, metadata, and a model execution script from the machine learning model package, identifying inputs of the model based on the metadata, obtaining input data, and executing the model using the model execution script and the input data to analyze the input data.

Abstract: The present disclosure describes a system, method, and computer readable medium capable of providing a data analysis/visualization application through which the user may seamlessly retrieve and analyze data from a variety of sources using a single interface. In one embodiment, this may be accomplished by applying data designations to at least a portion of the stored oilfield data and using the data designations to search for and retrieve oilfield data of interest to the user. In one embodiment, the data designations, and their association(s) with the oilfield data, may facilitate the retrieval and display of related actions such as calculations, alarms, workflow(s), notifications and unit conversions. In one embodiment, data designations may include one or more metadata flags applied to one or more pieces of oilfield data.