Oracle International Corporation

Amazon Technologies, Inc.

Motion Metrics International Corp.

Amin Suzani

Nipun Agarwal

Felix Schmidt

Andrew Brownsword

Matteo Casserini

Saeid Allahdadian

Milos Vasic

Shahram Tafazoli Bilandi

Jakub Wojciak

Mahdi Ramezani

Mohammad Sameti

Hamed Ahmadi

Saman Nouranian

Nazanin Hamzei

Saeed Karimifard

Neda Parnian

Matthew Alexander Baumann

Techniques are provided for sparse ensembling of unsupervised machine learning models. In an embodiment, the proposed architecture is composed of multiple unsupervised machine learning models that each produce a score as output and a gating network that analyzes the inputs and outputs of the unsupervised machine learning models to select an optimal ensemble of unsupervised machine learning models. The gating network is trained to choose a minimal number of the multiple unsupervised machine learning models whose scores are combined to create a final score that matches or closely resembles a final score that is computed using all the scores of the multiple unsupervised machine learning models.

Sparse ensembling of unsupervised models

Herein are feature extraction mechanisms that receive parsed log messages as inputs and transform them into numerical feature vectors for machine learning models (MLMs). In an embodiment, a computer extracts fields from a log message. Each field specifies a name, a text value, and a type. For each field, a field transformer for the field is dynamically selected based the field's name and/or the field's type. The field transformer converts the field's text value into a value of the field's type. A feature encoder for the value of the field's type is dynamically selected based on the field's type and/or a range of the field's values that occur in a training corpus of an MLM. From the feature encoder, an encoding of the value of the field's typed is stored into a feature vector. Based on the MLM and the feature vector, the log message is detected as anomalous.

Multi-stage feature extraction for effective ML-based anomaly detection on structured log data

Technologies are disclosed for providing a workload management service for first-in, first-out ('FIFO') queues in a network-accessible message queuing service within a service provider network. When a host is overloaded, or about to become overloaded, the affected host sends an alarm to the workload management service. The workload management service determines a set of other hosts that have lower workloads based upon performance metrics. The workload management service selects a new host from the set of hosts for moving a FIFO queue from the overloaded cluster. The workload management service seals the FIFO queue fragment at the overloaded host for writing of new messages and new messages having the same message group identifier are written to a new FIFO queue fragment on the new host. Messages are not read from the new FIFO queue fragment until all messages are read from the sealed FIFO queue fragment on the overloaded cluster.

Workload management service for first-in first-out queues for network-accessible queuing and messaging services

A method and apparatus for locating and/or determining the condition of a wear part in an image of an operating implement associated with heavy equipment is disclosed. The method involves capturing at least one image of the operating implement during operation of the heavy equipment, the image including a plurality of pixels each having an intensity value. The method also involves selecting successive pixel subsets within the plurality of pixels, and processing each pixel subset to determine whether pixel intensity values in the pixel subset meet a matching criterion indicating a likelihood that the pixel subset corresponds to the wear part. The matching criterion is based on processing a labeled set of training images during a training exercise prior to capturing the at least one image of the operating implement.

Method and apparatus for locating a wear part in an image of an operating implement

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Vancouver, Canada

Amin Suzani