Abstract: There are provided systems and methods for a fallback artificial intelligence (AI) system for redundancy during system failover. A service provider may provide AI systems for automated decision-making, such as for risk analysis, marketing, and the like. An AI system may operate in a production computing environment in order to provide AI decision-making based on input data, for example, by providing an output decision. In order to provide redundancy to the production AI system, the service provider may train a fallback AI system using the input/output data pairs from the production AI system. This may utilize a deep neural network and a continual learning trainer. Thereafter, when a failover condition is detected for the production AI system, the service provider may switch from the production AI system to the fallback AI system, which may provide decision-making operations during failure of within the production computing environment.

Abstract: A unidirectional workflow execution engine is accessed. The unidirectional workflow includes at least a first node and a second node, where the first node is executed before the second node. A timeout setting of the first node is retrieved. An actual execution time of the first node is determined. A difference between the retrieved timeout setting and the actual execution time of the first node is calculated. A timeout setting of the second node is retrieved. The timeout setting of the second node is modified by adding, to the retrieved timeout setting of the second node, the calculated difference between the retrieved timeout setting and the execution time of the first node.

Abstract: There are provided systems and methods for a fallback artificial intelligence (AI) system for redundancy during system failover. A service provider may provide AI systems for automated decision-making, such as for risk analysis, marketing, and the like. An AI system may operate in a production computing environment in order to provide AI decision-making based on input data, for example, by providing an output decision. In order to provide redundancy to the production AI system, the service provider may train a fallback AI system using the input/output data pairs from the production AI system. This may utilize a deep neural network and a continual learning trainer. Thereafter, when a failover condition is detected for the production AI system, the service provider may switch from the production AI system to the fallback AI system, which may provide decision-making operations during failure of within the production computing environment.

Abstract: Methods and systems are presented for dynamically configuring a software application to log different data variables without requiring re-deploying the software application. When a software application is executed, the software application may obtain and/or generate data variables. During runtime of the software application while processing a request, the software application may access a log script external to the software application. The software application may select a subset of data variables for logging, and may record only the subset of data variables in a log file. The log script may be modified without requiring the software application to be modified or re-deployed such that the software application may log different subset of data variables while processing different processes based on different versions of the log script.

Abstract: A unidirectional workflow execution engine is accessed. The unidirectional workflow includes at least a first node and a second node, where the first node is executed before the second node. A timeout setting of the first node is retrieved. An actual execution time of the first node is determined. A difference between the retrieved timeout setting and the actual execution time of the first node is calculated. A timeout setting of the second node is retrieved. The timeout setting of the second node is modified by adding, to the retrieved timeout setting of the second node, the calculated difference between the retrieved timeout setting and the execution time of the first node.

Abstract: A unidirectional workflow execution engine is accessed. The unidirectional workflow includes at least a first node and a second node, where the first node is executed before the second node. A timeout setting of the first node is retrieved. An actual execution time of the first node is determined. A difference between the retrieved timeout setting and the actual execution time of the first node is calculated. A timeout setting of the second node is retrieved. The timeout setting of the second node is modified by adding, to the retrieved timeout setting of the second node, the calculated difference between the retrieved timeout setting and the execution time of the first node.

Abstract: A unidirectional workflow execution engine is accessed. The unidirectional workflow includes at least a first node and a second node, where the first node is executed before the second node. A timeout setting of the first node is retrieved. An actual execution time of the first node is determined. A difference between the retrieved timeout setting and the actual execution time of the first node is calculated. A timeout setting of the second node is retrieved. The timeout setting of the second node is modified by adding, to the retrieved timeout setting of the second node, the calculated difference between the retrieved timeout setting and the execution time of the first node.