In some aspects, a computing system can determine a set of attributes based on analyzing input data using attribute templates written in a production-ready programming language. The computing system can generate attribute definitions for the set of attributes using the attribute templates and deploy the attribute definitions for the set of attributes to a production environment of a software program. The software program is written in a programming language compatible with the production-ready programming language. The computing system can monitor the performance of the set of attributes in the production environment of the software program and cause the attribute definitions of the plurality of attributes to be modified based on the monitoring.

Provided is a process of modeling methods organized in racks of a machine learning pipeline to facilitate optimization of performance using modelling methods for implementation of machine learning design in an object-oriented modeling (OOM) framework, the process including: writing classes using object-oriented modelling of optimization methods, modelling methods, and modelling racks; writing parameters and hyper-parameters of the modeling methods as attributes as the modeling methods; scanning modelling racks classes to determine first class definition information; selecting a collection of rack and selecting modeling method objects; scanning modelling method classes to determine second class definition information; assigning racks and locations within the racks to modeling method objects; and invoking the class definition information to produce object manipulation functions that allow access the methods and attributes of at least some of the modeling method objects, the manipulation functions being configured to effectuate writing locations within racks and attributes of racks.

Embodiments described herein relate to a software class object structure, system, and method for e-commerce gift card production, distribution and fulfillment in compliance with the three-tier regulatory structure for beverage alcohol.

Disclosed herein are various embodiments a code simplification system. An embodiment operates by receiving an identification of both a first code object and a second code object directed to producing a similar result. It is determined that the first code object and the second code object each include code subsets that correspond to each other. Differences between the code subsets of the first code object and the second code object are identified. The differences may include at least one of: a different ordering of the plurality of code subsets between the first code object and the second code object, or an additional code subset in the first code object that is not included in the second code object. A notification is provided with the differences, and input is received indicating an action to take by which either the first code object or the second code object are updated.

Case management systems and techniques are disclosed. In various embodiments, a trait definition is received that associates with a case node comprising a case model an object associated with an external system, e.g., a document or other content object and/or a business or other software object. The trait definition is used to bind respective instances of the object to corresponding instances of the case node in case instances created based on the case model.

Support for natural language expressions is provided by the use of semantic grammars that describe the structure of expressions in that grammar and that construct the meaning of a corresponding natural language expression. A semantic grammar extension mechanism is provided, which allows one semantic grammar to be used in the place of another semantic grammar. This enriches the expressivity of semantic grammars in a simple, natural, and decoupled manner.

In one example, a system can identify application programming interface (API) object classes specified in a definition file. The definition file can be for a target API object. The system can then update an attribute field associated with the target API object to specify the API object classes. Operator software in a distributed computing environment can be configured to monitor the API object classes specified in the attribute field of the target API object and execute computing logic in response to events related to the API object classes.

Systems, computer instructions and computer-implemented methods are disclosed for implementing space- and time-efficient enumerations. An instance of an enumeration class may be created with a constant, plurality of enumerations. A plurality of objects corresponding to the respective enumerations may be stored in memory along with a lookup table indexed by respective ordinal values of the plurality of enumerations, the lookup table including respective references to the stored objects of the instantiated enumeration class. A reference to an enumeration may be stored in a memory location by storing an ordinal value of the enumeration. A determination may then be made to convert a stored ordinal value to a reference to an object, and responsive to the determination, the ordinal value may be loaded and used as an index into the lookup table to obtain the reference to the object corresponding to the enumeration.

A system includes a memory and a processor, where the processor is in communication with the memory. The processor is configured to receive a request to build an application, where the application is associated with source code. The source code is analyzed to detect whether a first interface is implemented within the application. The source code is parsed to determine an invocation of the first interface. Inputs and outputs (I/Os) from the first interface are determined based on the invocation of the first interface. A model is generated based on the I/Os associated with the first interface, where the model includes a structure of each of the I/Os. The model is published to a registry.

Machine learning systems and associated methods are provided. A processor comprising at least one neural network can process a captured input image to translate the captured input image into an interactive demonstration presentation for an envisioned software product. The processing can include: automatically recognizing features within the captured input image; extracting the recognized features from the captured input image at the machine learning processor; processing each of the extracted features to determine a corresponding element in a library trained via a machine learning algorithm; and automatically replacing the extracted features from the captured input image with the one or more corresponding files or components to transform the captured input image into the interactive demonstration presentation.