Abstract: A method of using a computing device to manage a lifecycle of machine learning models includes receiving, by a computing device, multiple pre-defined machine learning lifecycle tasks. The computing device manages executing a management-layer software layer for the multiple pre-defined machine learning lifecycle tasks. The computing device further generates and updates a machine learning pipeline using the management-layer software layer.

Abstract: A lifecycle management method, system, and computer program product include coordinating hardware, platform and application-level health checks for framework-independent and application-specific monitoring, failure detection, and recovery, coordinating the hardware, the platform, and the application-level health check by state-specific aggregation of distributed atomic status events, and creating a recovery policy based on the state-specific aggregation of the distributed atomic status events.

Abstract: A lifecycle management method, system, and computer program product include establishing a public key infrastructure (PKI) for end-to-end encryption of control plane and data plane communications by providing encryption between arbitrary components for applicant execution where an interaction pattern is isolated, secure, and a multi-tenant environment.

Abstract: A lifecycle management method, system, and computer program product include coordinating hardware, platform and application-level health checks for framework-independent and application-specific monitoring, failure detection, and recovery, coordinating the hardware, the platform, and the application-level health check by state-specific aggregation of distributed atomic status events, and creating a recovery policy based on the state-specific aggregation of the distributed atomic status events.

Abstract: A lifecycle management method, system, and computer program product include establishing a public key infrastructure (PKI) for end-to-end encryption of control plane and data plane communications by providing encryption between arbitrary components for applicant execution where an interaction pattern is isolated, secure, and a multi-tenant environment.

Abstract: A machine-learning model generation method, system, and computer program product deciding, via a first algorithm, a machine-learning algorithm that is best for customer data, invoking the machine-learning algorithm to train a neural network model with the customer data, analyzing the neural network model produced by the training for an accuracy, and improving the accuracy by iteratively repeating the training of the neural network model until a customer-defined constraint is met, as determined by the first algorithm.

Abstract: The present invention provides a method, system and program product for invoking a remote method. Specifically, the invention provides an interface method object that may be used to map an application interface method call to a remote method call. The interface method object may be generated by retrieving an interface description for the remote method and using the interface description, including any necessary parameters determined from the interface description, to generate the interface method call. The interface method object may be generated dynamically at system runtime. The interface method object facilitates invocation of a remote method by mapping an interface method call from an application in interface method object format to a remote method call in the format of the remote method. Because the interface method call is in an interface method object format, changes to the underlying remote method do not affect the application's interface method call.

Abstract: A software module for use within an integrated circuit fabrication facility sends commands to a tool according to a method that operates with any tool that satisfies SEMI standards to add or subtract steps in a process without reprogramming or recompiling. The program operates autonomously to change parameters of commands that it sends to the tool to improve performance of the fab.