Oracle International Corporation

Vidya Mani

Vijayalakshmi Krishnamurthy

Amit Vaid

Nickolas Kavantzas

Mohan Kamath

Karthik Gvd

Sujay Sarkhel

Neelesh Kumar Shukla

Suresh Kumar Golconda

Someshwar Maroti Kale

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

Techniques for identifying anomalous multi-source data points and ranking the contributions of measurement sources of the multi-source data points are disclosed. A system obtains a data point including a plurality of measurements from a plurality of sources. The system determines that the data point is an anomalous data point based on a deviation of the data point from a plurality of additional data points. The system determines a contribution of two or more measurements, from the plurality of measurements, to the deviation of the data point from the plurality of additional data points. The system ranks the at least the two or more measurements, from the plurality of measurements, based on the respective contribution of each of the two or more measurements to the deviation of the anomalous data point from the plurality of prior data points.

Identifying and ranking anomalous measurements to identify faulty data sources in a multi-source environment

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Bengaluru, India

Vidya Mani