Abstract: Systems and/or techniques for facilitating online-monitoring of machine learning models are provided. In various embodiments, a system can receive monitoring settings associated with a machine learning model to be monitored. In various cases, the monitoring settings can identify a first set of data features that are generated as output by the machine learning model. In various cases, the monitoring settings can identify a second set of data features that are received as input by the machine learning model. In various aspects, the system can compute a first set of statistical metrics based on the first set of data features. In various cases, the first set of statistical metrics can characterize a performance quality of the machine learning model. In various instances, the system can compute a second set of statistical metrics based on the second set of data features.

Abstract: Systems and/or techniques for facilitating online-monitoring of machine learning models are provided. In various embodiments, a system can receive monitoring settings associated with a machine learning model to be monitored. In various cases, the monitoring settings can identify a first set of data features that are generated as output by the machine learning model. In various cases, the monitoring settings can identify a second set of data features that are received as input by the machine learning model. In various aspects, the system can compute a first set of statistical metrics based on the first set of data features. In various cases, the first set of statistical metrics can characterize a performance quality of the machine learning model. In various instances, the system can compute a second set of statistical metrics based on the second set of data features.

Abstract: Techniques are disclosed relating to the execution of queries in an online manner. For example, in some embodiments, a server system may include a distributed computing system that, in turn, includes a distributed storage system operable to store transaction data associated with a plurality of users, and a distributed computing engine operable to perform distributed processing jobs based on the transaction data. In various embodiments, the server system preemptively creates a compute session on the distributed computing engine, where the compute session provides access to various functionalities of the distributed computing engine. The distributed computing engine may then use these preemptively created compute sessions to execute queries (e.g., for end users of the server system) against the transaction data and return the results dataset to the requesting users in an online manner.

Abstract: Techniques are disclosed relating to the execution of queries in an online manner. For example, in some embodiments, a server system may include a distributed computing system that, in turn, includes a distributed storage system operable to store transaction data associated with a plurality of users, and a distributed computing engine operable to perform distributed processing jobs based on the transaction data. In various embodiments, the server system preemptively creates a compute session on the distributed computing engine, where the compute session provides access to various functionalities of the distributed computing engine. The distributed computing engine may then use these preemptively created compute sessions to execute queries (e.g., for end users of the server system) against the transaction data and return the results dataset to the requesting users in an online manner.

Abstract: Systems and/or techniques for facilitating online-monitoring of machine learning models are provided. In various embodiments, a system can receive monitoring settings associated with a machine learning model to be monitored. In various cases, the monitoring settings can identify a first set of data features that are generated as output by the machine learning model. In various cases, the monitoring settings can identify a second set of data features that are received as input by the machine learning model. In various aspects, the system can compute a first set of statistical metrics based on the first set of data features. In various cases, the first set of statistical metrics can characterize a performance quality of the machine learning model. In various instances, the system can compute a second set of statistical metrics based on the second set of data features.

Abstract: Systems and methods for optimizing filters for processing electronic services between end users are disclosed. In an embodiment, a computer system accesses scores corresponding to historic user actions. A first cutoff value and second cutoff value are determined for branches of a tree. A precision score for branches of the tree is calculated based on a number of historic user actions having chargebacks in relation to a number of historic user actions captured by the cutoff values of the branch. The computer system identifies a branch having a greatest precision score. The computer system determines that a threshold, defined by the first cutoff value and the second cutoff value for the identified branch, when used in a processing rule for a user account, changes a performance metric by a threshold amount. The computer system generates a recommendation for the user account to adjust a filter for the processing rule to use the threshold.

Abstract: Techniques are disclosed relating to the execution of queries in an online manner. For example, in some embodiments, a server system may include a distributed computing system that, in turn, includes a distributed storage system operable to store transaction data associated with a plurality of users, and a distributed computing engine operable to perform distributed processing jobs based on the transaction data. In various embodiments, the server system preemptively creates a compute session on the distributed computing engine, where the compute session provides access to various functionalities of the distributed computing engine. The distributed computing engine may then use these preemptively created compute sessions to execute queries (e.g., for end users of the server system) against the transaction data and return the results dataset to the requesting users in an online manner.

Abstract: Systems and/or techniques for facilitating online-monitoring of machine learning models are provided. In various embodiments, a system can receive monitoring settings associated with a machine learning model to be monitored. In various cases, the monitoring settings can identify a first set of data features that are generated as output by the machine learning model. In various cases, the monitoring settings can identify a second set of data features that are received as input by the machine learning model. In various aspects, the system can compute a first set of statistical metrics based on the first set of data features. In various cases, the first set of statistical metrics can characterize a performance quality of the machine learning model. In various instances, the system can compute a second set of statistical metrics based on the second set of data features.

Abstract: Techniques are disclosed relating to the execution of queries in an online manner. For example, in some embodiments, a server system may include a distributed computing system that, in turn, includes a distributed storage system operable to store transaction data associated with a plurality of users, and a distributed computing engine operable to perform distributed processing jobs based on the transaction data. In various embodiments, the server system preemptively creates a compute session on the distributed computing engine, where the compute session provides access to various functionalities of the distributed computing engine. The distributed computing engine may then use these preemptively created compute sessions to execute queries (e.g., for end users of the server system) against the transaction data and return the results dataset to the requesting users in an online manner.

Abstract: Systems and/or techniques for facilitating online-monitoring of machine learning models are provided. In various embodiments, a system can receive monitoring settings associated with a machine learning model to be monitored. In various cases, the monitoring settings can identify a first set of data features that are generated as output by the machine learning model. In various cases, the monitoring settings can identify a second set of data features that are received as input by the machine learning model. In various aspects, the system can compute a first set of statistical metrics based on the first set of data features. In various cases, the first set of statistical metrics can characterize a performance quality of the machine learning model. In various instances, the system can compute a second set of statistical metrics based on the second set of data features.

Abstract: Techniques are disclosed relating to the execution of queries in an online manner. For example, in some embodiments, a server system may include a distributed computing system that, in turn, includes a distributed storage system operable to store transaction data associated with a plurality of users, and a distributed computing engine operable to perform distributed processing jobs based on the transaction data. In various embodiments, the server system preemptively creates a compute session on the distributed computing engine, where the compute session provides access to various functionalities of the distributed computing engine. The distributed computing engine may then use these preemptively created compute sessions to execute queries (e.g., for end users of the server system) against the transaction data and return the results dataset to the requesting users in an online manner.