Hyper-parameter space optimization for machine learning data processing pipeline

US11544136B1 · kind B1 · utility

Assignee

• SAP SE · DE

Inventors

• Isil Pekel · Heidelberg, DE
• Steven Jaeger · Heidelberg, DE
• Manuel Zeise · Karlsruhe, DE

Key dates

• Filing date: Aug 5, 2021
• Grant date: Jan 3, 2023
• Expiry date: Aug 5, 2041

Classification

• Technology area (CPC G): Physics
• CPC primary: G06N20/20
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

A data processing pipeline may be generated to include an orchestrator node, a preparator node, and an executor node. The preparator node may generate a training dataset. The executor node may execute machine learning trials by applying, to the training dataset, a machine learning model and/or a different set of trial parameters. The orchestrator node may identify, based on a result of the machine learning trials, a machine learning model for performing a task. Data associated with the execution of the data processing pipeline may be collected for storage in a tracking database. A report including de-normalized and enriched data from the tracking database may be generated. The hyper-parameter space of the machine learning model may be analyzed based on the report. A root cause of at least one fault associated with the execution of the data processing pipeline may be identified based on the analysis.