公开(公告)号：US20240160939A1

公开(公告)日：2024-05-16

申请号：US17987518

申请日：2022-11-15

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： SATISH KUMAR MOPUR ,  KRISHNAPRASAD LINGADAHALLI SHASTRY ,  SATHYANARAYANAN MANAMOHAN ,  RAVI SARVESWARA ,  GUNALAN PERUMAL VIJAYAN

IPC: G06N3/08

CPC classification number: G06N3/088

Abstract: Anomalies and drift detection in decentralized learning environments. The method includes deploying at a first node, (1) a local unsupervised autoencoder, trained at the first node, along with a local training data reference baseline for the first node, and (2) a global unsupervised autoencoder trained across a plurality of nodes, along with a corresponding global training data reference baseline. Production data at the first node is processed with local and global ML models deployed by a user. At least one of local and global anomaly data regarding anomalous production data or local and global drift data regarding drifting production data is derived based on the local and global training data reference baselines, respectively. At least one of the local anomaly data is compared with the global anomaly data or the local drift data with the global drift data for assessing impact of anomalies/drift on the ML models.

公开(公告)号：US20240312180A1

公开(公告)日：2024-09-19

申请号：US18184465

申请日：2023-03-15

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： SATISH KUMAR MOPUR ,  GUNALAN PERUMAL VIJAYAN ,  SHOUNAK BANDOPADHYAY ,  VIJAYA SHARVANI HINDNAVIS ,  KRISHNAPRASAD LINGADAHALLI SHASTRY

IPC: G06V10/762 ,  G06V10/26 ,  G06V10/42

CPC classification number: G06V10/762 ,  G06V10/26 ,  G06V10/42

Abstract: Systems and methods for preventing prediction performance degradation by detecting and extracting skews in data during both training and production environments is described herein. Feature extraction may be performed on training data during the training phase, followed by pattern analysis that assesses similarities across labeled training data sets. A reference pattern may be derived from the pattern analysis and feature extraction of the training data. Feature extraction and pattern analysis may be performed on production data during the serving phase, and a target pattern may be derived from the pattern analysis and feature extraction of the production data. The reference pattern and target pattern may be fed to a discrepancy detection functionality to detect discrepancies by using a sliding window to move the target pattern across the reference pattern to make comparisons between the patterns. The comparison may provide a quantitative skew across the training and production data.

公开(公告)号：US20200050578A1

公开(公告)日：2020-02-13

申请号：US16100076

申请日：2018-08-09

Applicant: Hewlett Packard Enterprise Development LP

Inventor： SATISH KUMAR MOPUR ,  SAIKAT MUKHERJEE ,  GUNALAN PERUMAL VIJAYAN ,  SRIDHAR BALACHANDRIAH ,  ASHUTOSH AGRAWAL ,  KRISHNAPRASAD LINGADAHALLI SHASTRY ,  GREGORY S. BATTAS

IPC: G06F15/18 ,  G06F9/54 ,  H04L29/08

Abstract: The disclosure relates to technology that implements flow control for machine learning on data such as Internet of Things (“IoT”) datasets. The system may route outputs of a data splitter function performed on the IoT datasets to a designated target model based on a user specification for routing the outputs. In this manner, the IoT datasets may be dynamically routed to target datasets without reprogramming machine-learning pipelines, which enable rapid training, testing and validation of ML models as well as an ability to concurrently train, validate, and execute ML models.