Abstract: Disclosed herein are system, method, and computer program product embodiments for providing application resiliency using a machine learning model trained to detect potential failures based on computational transaction metrics. A resiliency system may monitor metrics related to an application executing on an enterprise data system. The resiliency system may apply these metrics to a machine learning model trained to identify a potential application failure based on application usage trends. In response to detecting a potential failure of the application, the resiliency system may instruct the application to execute one or more resiliency actions. These may include one or more circuit breaker, rate limiter, time limiter, and/or bulkhead actions. The resiliency actions may aid the application in avoiding failure states. The resiliency actions may also be modified based on feedback metrics to aid the application in quickly restoring service once the failure state has been avoided.

Abstract: Disclosed herein are system, method, and computer program product embodiments for extracting and tracking metadata from a data store. For example, the method includes extracting a plurality of identifiers of data from a data source. An identifier uniquely identifies a record in the data source. The method further includes scanning the data to extract a plurality of data samples, extracting metadata from each data sample of the plurality of data samples, hashing the metadata of each respective data sample to generate a respective hash value associated with each respective data sample of the plurality of data samples, comparing the hash values to identify one or more unique hash values, identifying one or more unique schemas corresponding to the unique hash value, and storing the one or more unique schemas in a data store. The metadata comprises schema indicative of one or more attributes of each respective data sample.

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 configurable and event driven smart hybrid cloud orchestrators. Various embodiments include a first computing device that can receive a request that includes various configurations to perform one or more actions. The first computing device can generate machine-readable instructions to perform a first action. The first computing device can send a first event to an event hub that includes the machine-readable instructions. The second computing device can receive the first event from the event hub and provision computing resources. The second computing device can then modify the machine-readable instructions based on the computing resources and then execute the machine-readable instructions. The second computing device can send a second event indicating that the first action has completed. The first computing device can receive the second event from the event hub.

Abstract: Disclosed herein are system, method, and computer program product embodiments for converting queries and scripts used in big data systems. A script conversion system converts a script and its queries from a first big data format to a second big data format. The script conversion system uses a configuration mapping that includes direct and/or indirect function mappings. For the direct mapping, the script conversion system replaces the function with a new function in the second big data format. For the indirect mapping, the script conversion system constructs an equivalent function in the second big data format using information extracted from parsing queries in the first big data format. The script conversion system also formats a converted query using a project mapping and/or generates a conversion report indicating successful or unsuccessful conversions. Scripts may be converted from an on-premises data warehouse system to a cloud-based data warehouse system.

Abstract: A computer-based system may engineer features based on semantic types. The computer-based system may implement deep learning algorithms and derive a domain-specific feature engineering strategy from semantic type predictions and data profiling. The computer-based system may utilize embedded domain (e.g., financial industry, etc.) knowledge to generate curated features from raw data (e.g., transactional datasets, relational datasets, etc.).

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 computer-based system may engineer features based on semantic types. The computer-based system may implement deep learning algorithms and derive a domain-specific feature engineering strategy from semantic type predictions and data profiling. The computer-based system may utilize embedded domain (e.g., financial industry, etc.) knowledge to generate curated features from raw data (e.g., transactional datasets, relational datasets, etc.).

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 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.