Abstract: A method for generating predictive and event-based action recommendations includes receiving, from at least one data source, input data representative of a user; classifying, using a first machine learning model, the input data based on one or more personas representative of user characteristics to generate classified input data; identifying, from the input data, at least one event associated with the user; and generating, based on the at least one event and the classified input data, a personalized recommendation for the user using one or more second machine learning models, wherein the personalized recommendation comprises a set of actions predicted to achieve the goal based on the impact on the goal caused by the at least one event.

Abstract: A method for generating predictive and event-based action recommendations includes receiving, from at least one data source, input data representative of a user; generating, using a first machine learning model, a user behavior pattern for the user based on the input data representative of the user; classifying the user behavior pattern based on one or more personas representative of user characteristics; identifying, based on at least the input data and the one or more personas, an inefficiency in the user behavior pattern impacting a goal of the user; and generating, using a second machine learning model, a personalized recommendation for the user to remediate the inefficiency.

Abstract: A system includes a memory, and a processing device, operatively coupled to the memory, to perform operations including obtaining user data associated with a user, generating, using at least one machine learning (ML) model processing the user data, a virtual environment associated with an extended reality (XR) representation corresponding to a personalized recommendation for the user, and creating, based on the user data, a virtual avatar for the user reflecting a representation of the user within the virtual environment. The virtual avatar corresponds to a set of attributes representing visual features of the user.

Abstract: A system includes a memory, and a processing device, operatively coupled to the memory, to perform operations including receiving, from a user device associated with a user, a request to generate a personalized recommendation for the user, in response to receiving the request, obtaining user data associated with the user from a set of data sources, generating, using at least one machine learning model based on the user data, the personalized recommendation and an extended reality (XR) representation of the personalized recommendation accessible to the user via an XR system, and causing the personalized recommendation and the XR representation to be sent to the user.

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: 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 embodiments for garbage collection for object-based storage systems. A first set of objects stored in a data object store is identified as not meeting criteria for deletion or archiving, the criteria specified in a retention policy. A second set of objects is identified by performing a search of the object data store for data objects belonging to a data structure with the first set of objects. The second set of objects is recorded as not meeting the criteria for deletion or archiving. The second set of data objects are retained, based on the recording, to prevent the data structure from being corrupted.

Abstract: Techniques for implementing digital wallet applications supporting decentralized web integration are disclosed herein. An exemplary system includes: one or more processors; and a non-transitory computer-readable memory coupled to the one or more processors. The memory stores instructions thereon that, when executed by the one or more processors, cause the one or more processors to: initialize a digital wallet application associated with a user, wherein the digital wallet application is integrated with at least one decentralized web technology and hosted on a digital wallet platform. The digital wallet application also implements a digital wallet linked to a set of electronic accounts. The instructions further cause the one or more processors to obtain data from a set of data sources; and generate, using at least one machine learning (ML) model based on the data, a recommendation for the user related to at least one electronic account of the set of electronic accounts.

Abstract: Disclosed are various embodiments for garbage collection for object-based storage systems. A first set of objects stored by an object storage service that have been accessed within a previously defined date range is identified. Then, a second set of objects stored by the object storage service is identified based at least in part on a relationship to one or more objects in the first set of objects. Next, a third set of objects stored by the object storage service that have been created prior to a predefined date is identified. Then, a subset of objects which are members of the third set of objects and not members of the first set of objects or the second set of objects is identified. Finally, a retention action is performed on individual members of the subset of objects based at least in part on a retention policy.

Abstract: A system for generating graphical relationship maps is disclosed. The system may receive a search request. The system may generate a relationship map data based on linked data elements of at least one of a structured data set, an unstructured data set, and a hybrid data set comprising structured and unstructured data. The system may display a graphical relationship map including an entity type icon based on the relationship map data and the search request.

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: A system for generating graphical relationship maps is disclosed. The system may receive a search request. The system may generate a relationship map data based on linked data elements of at least one of a structured data set, an unstructured data set, and a hybrid data set comprising structured and unstructured data. The system may display a graphical relationship map including an entity type icon based on the relationship map data and the search request.

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: Disclosed are various embodiments for garbage collection for object-based storage systems. A first set of objects stored by an object storage service that have been accessed within a previously defined date range is identified. Then, a second set of objects stored by the object storage service is identified based at least in part on a relationship to one or more objects in the first set of objects. Next, a third set of objects stored by the object storage service that have been created prior to a predefined date is identified. Then, a subset of objects which are members of the third set of objects and not members of the first set of objects or the second set of objects is identified. Finally, a retention action is performed on individual members of the subset of objects based at least in part on a retention policy.

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: 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 embodiments for garbage collection for object-based storage systems. A first set of objects stored by an object storage service that have been accessed within a previously defined date range is identified. Then, a second set of objects stored by the object storage service is identified based at least in part on a relationship to one or more objects in the first set of objects. Next, a third set of objects stored by the object storage service that have been created prior to a predefined date is identified. Then, a subset of objects which are members of the third set of objects and not members of the first set of objects or the second set of objects is identified. Finally, a retention action is performed on individual members of the subset of objects based at least in part on a retention policy.

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: A system for data migration is disclosed. The system may receive a migration request comprising a source file path and a target file location. The system may capture source file metadata based on the source file path and the migration request. The system may transfer a source file from a first data environment to an intermediate data environment via a first transfer process. The system may transfer the source file from the intermediate data environment to a second data environment via a second transfer process.

Abstract: The method includes receiving historical data from a first data source; analyzing the historical data for a desired characteristic; determining a representative value for the desired characteristic of the historical data; determining a first data expectation for the historical data based on the representative value; transmitting the first data expectation to a first data recipient; receiving first incoming data from the first data source; analyzing the desired characteristic of the first incoming data; determining a first incoming data value for the desired characteristic for the first incoming data; comparing the first incoming data value and the representative value; determining a first difference between the first incoming data value and the representative value; and/or comparing the first difference to a difference threshold which indicates whether a difference between an incoming data value and the representative value is significant.