Abstract: This disclosure is directed to a generalizable machine learning model production environment and system with a defense mechanism that facilitates safe execution of machine learning models in production by effectively detecting potential known and new adversarial attacks. The disclosed exemplary systems and architectures gather data from the online execution of the machine learning models and communicate with an on-demand pipelines for further inspection and/or correction of vulnerabilities in the production machine learning model to the detected attacks. These systems and architectures provide an automatable process for continuous monitoring of model performance and correction of the production machine learning model to guard against current and future adversarial attacks.

Abstract: A biomarker, for diagnosing pancreatic cancer, including asprosin, and the use thereof, has been confirmed that: the survival prognosis of a pancreatic cancer patient is worse in the case of a higher expression of FBN-1 gene encoding fibrillin-1 which is the precursor of asprosin in a clinical information database; an asprosin level at a cell level is related to an occurrence of pancreatic cancer; and the asprosin level in the blood of a pancreatic cancer patient or an early stage pancreatic cancer patient is higher than that of a healthy person.

Abstract: A resource prediction system for executing machine learning models and method are provided. The system includes non-transitory memory storing instructions and a processor configured to execute the instructions to obtain input data including a targeted objective and the constraints, select a deployable machine learning model having an evaluation score that meets a predetermined criterion from among candidate machine learning models, virtually execute the deployable machine learning model on each of candidate hardware platforms according to the constraints, generate an assessment report of the virtual performance metrics set of the deployable machine learning model executed on each of the candidate hardware platforms, and select the suggested hardware platform meeting the predetermined criterion from among the candidate hardware platforms.

Abstract: The present disclosure describes a system, a method, and a product for computational resource prediction of user tasks and subsequent workload provisioning. The computational resource predictions for a user task is achieved using a twin machine learning and AI system based on probabilistic programing. The workload scheduling and assignment of the user task in a computing cluster with components having diverse hardware architectures are further managed by an automatic and intelligent assignment/provisioning engine based on various machine learning and AI models and reinforcement learning. The automatic workload scheduling and assignment engine is further configured to handle unpredicted uncertainty and adapt to constantly evolving system queues of the tasks submitted by the users to generate queuing/re-queuing, running/termination, and resource allocation/reallocation actions for user tasks.

Abstract: Complex computer system architectures are described for detecting a concept drift of a machine learning model in a production environment, for adaptive optimization of the concept drift detection, for extracting embedded features associated with the concept drift using a shadow learner, and for adaptive adjustment of the machine learning model in production to mitigate the effect of predictive performance drop due to the concept drift.

Abstract: This disclosure is directed to a generalizable machine learning model production environment and system with a defense mechanism that facilitates safe execution of machine learning models in production by effectively detecting potential known and new adversarial attacks. The disclosed exemplary systems and architectures gather data from the online execution of the machine learning models and communicate with an on-demand pipelines for further inspection and/or correction of vulnerabilities in the production machine learning model to the detected attacks. These systems and architectures provide an automatable process for continuous monitoring of model performance and correction of the production machine learning model to guard against current and future adversarial attacks.