International Business Machines Corporation

Anupama Murthi

Evelyn Duesterwald

Jiri Navratil

Vijay Arya

Matthew R Arnold

Michael James Hind

Deepak Vijaykeerthy

Waldemar Hummer

Debashish Saha

Punleuk Oum

Ganesh Venkataraman

Benjamin Tyler Elder

Gaodan Fang

A computer implemented method of performing large-scale machine learning experiments includes expanding on one or more input datasets by systematically generating several data set drift splits. A set of experimental jobs corresponding to the generated data set drift splits are executed to generate experimental results. The experimental results are processed, consolidated, and clustered according to the generated data set drift splits.

Assistant for large-scale machine learning experiments

In an embodiment, a method for inspecting and transforming a machine learning model includes receiving a request that includes the machine learning model and a configuration object that provides an indication of a selected strategy. In the embodiment, the method includes creating a partially specified task graph that includes a first placeholder node for a future expanded task node. In the embodiment, the method includes performing a dynamic expansion and execution phase that includes, repeatedly (a) using a cognitive engine to evaluate whether to revise the partially specified task graph based at least in part on the selected strategy, and (b) using a processor-based execution engine to perform an action specified by the complete node. In an embodiment, the dynamic expansion and execution phase repeats until after the cognitive engine adds a consolidated results node.

System for operationalizing high-level machine learning training enhancements from low-level primitives

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

Growing community of inventors

Pleasantville, NY, United States of America

Anupama Murthi