International Business Machines Corporation

Debashish Saha

Punleuk Oum

Evelyn Duesterwald

Archit Verma

Gaodan Fang

Liana Liyow Fong

Seetharami R Seelam

Darrell Christopher Reimer

Benjamin W Herta

Waldemar Hummer

Prabhat Maddikunta Reddy

Anupama Murthi

Ganesh Venkataraman

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.

Configurable and scalable machine learning model lifecycle operator

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.

Elastic execution of machine learning workloads using application based profiling

A computer-implemented method includes obtaining data associated with execution of a model deployed in a computing environment. At least a portion of the obtained data is analyzed to detect one or more failure conditions associated with the model. One or more restoration operations are executed to generate one or more restoration results to address one or more detected failure conditions. At least a portion of the one or more restoration results is sent to the computing environment in which the model is deployed.

Detection of failure conditions and restoration of deployed models in a computing environment

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White Plains, NY, United States of America

Debashish Saha