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: An approach is disclosed for storing information tailored to a user and a condition to be accessed offline to fit on a local device. The information is retrieved from a set of online sources by a device. The retrieved information is tailored according to a criteria and the condition to form a curated data. An index is provided to access the curated data. The index is used to answer queries. Responsive to identifying a question without an answer found in the curated data when the device is off-line, searching for the answer when the device is online. Responsive to finding the answer online, pruning the curated data by eliminating lower importance data as needed to store higher importance data based on an availability of space allocated to store the curated data on the local storage device, and updating the curated data stored on the local device.

Abstract: Embodiments relate to a system, program product, and method for supporting elastic execution of a machine learning (ML) workload using application based profiling. A joint profile comprised of both ML application execution and resource usage data is generated. One or more feature(s) and signature(s) from the joint profile are identified, and a ML execution model for ML application execution and resource usage is built. The ML execution model leverages the feature(s) and signature(s) and is applied to provide one or more directives to subsequent application execution. The application of the ML execution model supports and enables the ML execution to elastically allocate and request one or more resources from a resource management component, with the elastic allocation supporting application execution.

Abstract: An approach is disclosed for a method, a system, and a computer program product for storing information tailored to a user and a condition to be accessed offline on a local device. The information is retrieved from a set of online sources. The retrieved information is tailored according to a criteria and the condition to form a curated data. The curated data is on the local device. An index is provided to access a selected portion of the curated data. The index is used to answer queries from the user. Responsive to identifying a question without an answer identified in the retrieved content, searching for the answer when the device is online. Responsive to finding the answer online, updating the curated data stored on the local device.

Abstract: Embodiments relate to a system, program product, and method for supporting elastic execution of a machine learning (ML) workload using application based profiling. A joint profile comprised of both ML application execution and resource usage data is generated. One or more feature(s) and signature(s) from the joint profile are identified, and a ML execution model for ML application execution and resource usage is built. The ML execution model leverages the feature(s) and signature(s) and is applied to provide one or more directives to subsequent application execution. The application of the ML execution model supports and enables the ML execution to elastically allocate and request one or more resources from a resource management component, with the elastic allocation supporting application execution.