Abstract: Disclosed are various embodiments for using a distributed ledger to track the evolution or deployment of feature sets for machine-learning. An approval request to approve creation of a feature set record in a distributed ledger can be received. The approval request can include a feature set hash representing a feature set, a code hash representing a feature set generator, and a data hash representing a data set. An approval decision can be generated for the feature set record based at least in part on the approval request and at least one approval rule stored in an approval data store. The approval decision can be transmitted to a distributed agent. The approval decision can approve or reject creation of the feature set record in the distributed ledger.

Abstract: Systems and methods for identity-based interconnected transactions are disclosed. The system may receive an interconnected transaction request comprising a first financial subsystem identifier, a second financial subsystem identifier, and a transaction request. The first financial subsystem and the second financial subsystem may not be in direct communication with each other. The first financial subsystem may write the interconnected transaction request to an interconnected transaction ledger. The second financial subsystem may retrieve the interconnected transaction request from the interconnected transaction ledger. In response to retrieving the interconnected transaction request the second financial subsystem may complete the transaction request.

Abstract: Systems and methods for automated data extraction and adaptation are disclosed. The system may receive a data input from an external source using various different input channels. The system may determine a data quality of the data input by comparing data fields of the data input to known metadata in the system. The system may reformat the data input based on the comparison to a format consumable by downstream applications and services. The system may apply various machine learning operations on the data input including a descriptive analytics analysis, a predictive learning analysis, and/or a prescriptive intelligence analysis.

Abstract: Disclosed are various embodiments for using a distributed ledger to track the evolution or deployment of feature sets for machine-learning. A registration request is received from a first node of a distributed ledger, the registration request comprising a code hash representing a feature set generator and a data hash representing a data set. The registration request is then relayed to a second node of the distributed ledger for approval by the second node. Next, an approval for the registration request is received from the second node. Subsequently, an entry in the distributed ledger comprising the code hash and the data hash is created.

Abstract: Systems and methods for automated data extraction and adaptation are disclosed. The system may receive a data input from an external source using various different input channels. The system may determine a data quality of the data input by comparing data fields of the data input to known metadata in the system. The system may reformat the data input based on the comparison to a format consumable by downstream applications and services. The system may apply various machine learning operations on the data input including a descriptive analytics analysis, a predictive learning analysis, and/or a prescriptive intelligence analysis.

Abstract: Disclosed are various approaches for managing the status of machine-learning models using distributed ledgers. A registration request for a machine-learning model can be received. The registration request can include a model name for the machine-learning model, a version identifier for the machine-learning model, a network address from which the machine-learning model can be retrieved, a source code hash for a source code version of the machine learning model, and a runtime hash for a binary executable version of the machine-learning model. A registration identifier can then be created based at least in part on the source code hash and the runtime hash. Subsequently, an entry in the distributed ledger can be created for the machine-learning model. The entry can include the registration identifier, the model name, the model version, the network address, the source code hash, and the runtime hash.

Abstract: Disclosed are various approaches for managing the status of machine-learning models using distributed ledgers. A registration request for a machine-learning model can be received. The registration request can include a model name for the machine-learning model, a version identifier for the machine-learning model, a network address from which the machine-learning model can be retrieved, a source code hash for a source code version of the machine learning model, and a runtime hash for a binary executable version of the machine-learning model. A registration identifier can then be created based at least in part on the source code hash and the runtime hash. Subsequently, an entry in the distributed ledger can be created for the machine-learning model. The entry can include the registration identifier, the model name, the model version, the network address, the source code hash, and the runtime hash.

Abstract: Systems and methods for data Process an optimized analytics environment are disclosed. The system may enable users to create data processing requests, interact with various data sources and datasets, and generate data processing outputs. The system may receive a data processing request from an audio-enabled input source or a UI-based input source. The system may determine whether the data processing request at least partially matches a stored data processing request. The system may receive a data processing request selection comprising the data processing request or the stored data processing request. The system may execute the data processing request selection on a data source.

Abstract: Systems and methods for data processing in an optimized analytics environment are disclosed. The system may enable users to create data processing requests, interact with various data sources and datasets, and generate data processing outputs. The system may receive a data processing request from an audio-enabled input source or a UI-based input source. The system may determine whether the data processing request at least partially matches a stored data processing request. The system may receive a data processing request selection comprising the data processing request or the stored data processing request. The system may execute the data processing request selection on a data source.

Abstract: A master processor may retrieve historical and real time machine and human data related to computer system health. The master processor may utilize machine learning and artificial intelligence to predict potential computer malfunctions. The master processor may output notifications regarding the potential computer malfunctions in order to prevent the computer malfunctions from occurring.

Abstract: Systems and methods for identity-based interconnected transactions are disclosed. The system may receive an interconnected transaction request comprising a first financial subsystem identifier, a second financial subsystem identifier, and a transaction request. The first financial subsystem and the second financial subsystem may not be in direct communication with each other. The first financial subsystem may write the interconnected transaction request to an interconnected transaction ledger. The second financial subsystem may retrieve the interconnected transaction request from the interconnected transaction ledger. In response to retrieving the interconnected transaction request the second financial subsystem may complete the transaction request.

Abstract: Systems and methods for data processing in an optimized analytics environment are disclosed. The system may enable users to create data processing requests, interact with various data sources and datasets, and generate data processing outputs. The system may receive a data processing request from an audio-enabled input source or a UI-based input source. The system may determine whether the data processing request at least partially matches a stored data processing request. The system may receive a data processing request selection comprising the data processing request or the stored data processing request. The system may execute the data processing request selection on a data source.

Abstract: Systems and methods for automated data extraction and adaptation are disclosed. The system may receive a data input from an external source using various different input channels. The system may determine a data quality of the data input by comparing data fields of the data input to known metadata in the system. The system may reformat the data input based on the comparison to a format consumable by downstream applications and services. The system may apply various machine learning operations on the data input including a descriptive analytics analysis, a predictive learning analysis, and/or a prescriptive intelligence analysis.

Abstract: A master processor may retrieve historical and real time machine and human data related to computer system health. The master processor may utilize machine learning and artificial intelligence to predict potential computer malfunctions. The master processor may output notifications regarding the potential computer malfunctions in order to prevent the computer malfunctions from occurring.

Abstract: Systems and methods for executing tasks using modular code containers, intelligent code containers and data containers are provided. The system may comprise a master coordinator configured to implement algorithmic intelligence, artificial intelligence, and/or machine learning to intelligently generate, store, maintain, and retrieve the modular code containers and data containers. The system may receive task requests having at least one feature comprising functional components and data components. The system may retrieve the code containers based on the functional components, and the data containers based on the data components. The system may generate a result set by executing code from the code container to process data from the data container.

Abstract: A system and method for automated continuous task triggering is provided. The system may provide automated continuous task triggering by monitoring a data source and a data output, retrieving a task criteria from a criteria database, and executing a task in response to the task criteria being met. The task criteria may comprise a set of instructions to execute the task on the data source and the data output. The system may also recalibrate the task criteria based on the executing of the task to generate a recalibrated task criteria.

Abstract: Systems and methods for health monitoring and task agility within network environments are provided. The system may comprise a master data controller configured to monitor servers located in an environment level of a network environment. The master data controller may also monitor tasks located on each server. The master data controller may detect a change in a task feature related to the task. The master data controller may also detect a change in an environment feature related to the servers. The master data controller may recalibrate the task in response to the change in the task feature or the change in the environment feature.

Abstract: A system and method for automated continuous task triggering is provided. The system may provide automated continuous task triggering by monitoring a data source and a data output, retrieving a task criteria from a criteria database, and executing a task in response to the task criteria being met. The task criteria may comprise a set of instructions to execute the task on the data source and the data output. The system may also recalibrate the task criteria based on the executing of the task to generate a recalibrated task criteria.