Oracle International Corporation

Someshwar Maroti Kale

Nickolas Kavantzas

Vijayalakshmi Krishnamurthy

Amit Vaid

Mohan Kamath

Sujay Sarkhel

Neelesh Kumar Shukla

Vidya Mani

Suresh Kumar Golconda

Techniques for selecting universal hyper parameters for use in a set of machine learning models across multiple computing environments include detection of a triggering condition for tuning a set of universal hyper parameters. The set of universal hyper parameters dictate configuration of the set of machine learning models that are independently executing, respectively, in the multiple computing environments. Based on the detected triggering condition, a first subset of universal hyper parameters from the set of universal hyper parameters are altered to generate a second set of universal hyper parameters. The second set of universal hyper parameters are applied to the set of machine learning models across the multiple computing environments.

Hyper parameter tuning for machine learning models

Growing community of inventors

Bengaluru, India

Someshwar Maroti Kale