International Business Machines Corporation

Jason Tsay

Martin J Hirzel

Sameep Mehta

Julian Timothy Dolby

Ramya Raghavendra

Chuang Gan

Dakuo Wang

Kalapriya Kannan

Kush Raj Varshney

Ming Tan

John Thomas Richards

Aleksandra Mojsilovic

Darrell Christopher Reimer

Todd W Mummert

Rachel K E Bellamy

Manish Kesarwani

Avraham Ever Shinnar

Matthew R Arnold

Kaoutar El Maghraoui

Michael James Hind

Gregory Bramble

Stephanie Houde

Alan Braz

David John Piorkowski

Techniques regarding transferring learning outcomes across machine learning tasks in automated machine learning systems are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a transfer learning component that can executes a machine learning task using an existing artificial intelligence model on a sample dataset based on a similarity between the sample dataset and a historical dataset. The existing artificial intelligence model can be generated by automated machine learning and trained on the historical dataset.

Transfer learning across automated machine learning systems

In an approach to improve detecting and correcting errors in one or more machine learning pipelines. Embodiments comprise generating a plurality of test machine learning pipeline instances based upon a target machine learning pipeline and evaluating the plurality of test machine learning pipeline instances for failure in a task. Further, embodiments identify one or more root causes of error based upon the evaluated plurality of test machine learning pipeline instances and failure in the task, and create a remediated target machine learning pipeline based upon the identified one or more root causes of error. Additionally, embodiments output the remediated machine learning pipelines.

Repairing machine learning pipelines

A machine-learning model task deduction method, system, and computer program product include extracting data schema of a machine-learning model and analyzing the data schema to determine an intended task of the machine-learning model.

Schema-based machine-learning model task deduction

A method for automatically generating documentation for an artificial intelligence model includes receiving, by a computing device, an artificial intelligence model. The computing device accesses a model facts policy that indicates data to be collected for artificial intelligence models. The computing device collects artificial intelligence model facts regarding the artificial intelligence model according to the model facts policy. The computing device accesses a factsheet template. The factsheet template provides a schema for an artificial intelligence model factsheet for the artificial intelligence model. The computing device populates the artificial intelligence model factsheet using the factsheet template with the artificial intelligence model facts related to the artificial intelligence model.

Generation and management of an artificial intelligence (AI) model documentation throughout its life cycle

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White Plains, NY, United States of America

Jason Tsay