Abstract: The disclosed technology is generally directed to virtual machines. In one example of the technology, a network change from a first virtual network to a second virtual network is reconfigured for a first virtual machine that is executing on a first virtual machine host. The reconfiguring includes the following. In the first virtual machine host, a mapping change from the first virtual network to the second virtual network is configured by reprogramming drivers in the first virtual machine host for route mapping for the second virtual network. A Dynamic Host Configuration Protocol (DHCP) retrigger is caused without rebooting the first virtual machine. A configuration file is provided to the first virtual machine. The configuration file includes user-specific networking settings. The first virtual machine is reconfigured in accordance with the user-specific networking settings.

Abstract: Systems, methods, and apparatuses for automatically detecting the risk of dealer default are described. A machine learning model may be trained using a gradient boosting technique on a dataset that includes historical lien information, historical dealer information, and historical vehicle default information. Lien information and financing information for vehicles may be received and correlated. Furthermore, a machine learning model may generate aggregate risk scores for dealers. Furthermore, determination of an aggregate risk score that exceeds a threshold risk score may cause the generation of a notification.

Abstract: Systems, methods, and apparatuses for automatically prioritizing unperfected liens are described. A machine learning model may be trained to detect a risk of dealer default. Lien information corresponding to liens for vehicles associated with dealers may be received. Prioritized lien information may be generated based on selection of the liens that satisfy criteria. Financing information for vehicles may be received and correlated with the prioritized lien information. Furthermore, the machine learning model may be used to generate risk scores associated with the liens. The liens may be prioritized based on the aggregate risk scores.

Abstract: Methods and systems are for generating real-time resolutions of errors arising from user submissions, computer processing tasks, etc. For example, the methods and systems described herein recite improvements for detecting errors in one or more user submissions and providing resolutions in real-time. To provide these improvements, the methods and systems use a machine learning model that is trained to return probability error scores based on a plurality of variables. By using the multivariate approach, the methods and systems may produce a highly accurate detection.

Abstract: Methods and systems are described for improvements to the use of distributed computer networks. For example, conventional systems may rely on the distribution of network or application traffic across multiple servers and may maintain load balancers to maintain that distribution in an efficient manner. Each load balancer may sit between client devices and backend servers, receiving and then distributing incoming requests to any available server capable of fulfilling them. The load balancers may ensure that no one server is overworked based on the number of processing requests directed to that server, which could degrade performance.

Abstract: Methods and systems are described for improvements to the use of distributed computer networks. For example, conventional systems may rely on the distribution of network or application traffic across multiple servers and may maintain load balancers to maintain that distribution in an efficient manner. Each load balancer may sit between client devices and backend servers, receiving and then distributing incoming requests to any available server capable of fulfilling them. The load balancers may ensure that no one server is overworked based on the number of processing requests directed to that server, which could degrade performance.

Abstract: Methods and systems are described for improvements to the use of distributed computer networks. For example, conventional systems may rely on the distribution of network or application traffic across multiple servers and may maintain load balancers to maintain that distribution in an efficient manner. Each load balancer may sit between client devices and backend servers, receiving and then distributing incoming requests to any available server capable of fulfilling them. The load balancers may ensure that no one server is overworked based on the number of processing requests directed to that server, which could degrade performance.

Abstract: Disclosed herein are system, method, and computer program product embodiments for generating optimized data structure objects. In a given embodiment, a server receives a request to identify a set of data structure objects from a plurality of data structure objects for a specified vehicle and a specified user and based on user objective data and an attribute. The server may generate the plurality of data structure objects within constraints of the user objective data, policy data, and the attribute. The server may identify the set of data structure objects from the plurality of data structure objects. Each data structure object of the set is within a threshold distance to a value of at least one dimension of a set of the dimensions of the set of dimensions of a model.