Back to results
US 12,676,823 B1Granted
Dynamically provisioning resources for detecting anomalies in time series data using machine learning models
AssigneeAmazon Technologies, Inc.
InventorsRanjith Kumar Bodla, Lakshay Kharbanda, Sadanand Murthy Sachidananda, Ashutosh Gupta, Gaurav Rajendra Kataria, Ketan Vijayvargiya
Life of this patent
Estimating the term…
Abstract
Computing resources for anomaly detection on time series data using a machine learning model may be dynamically provisioned. An expected workload of anomaly detection on time series data is determined in order to determine a number of computing resources. The number of computing resources are provisioned for anomaly detection and updated when the machine learning model is re-trained.
Claims20
A system, comprising:
- at least one processor;
- and
- a memory, storing program instructions that when executed by the at least one processor, cause the at least one processor to implement:
- receive a request to enable anomaly detection using a machine learning model to detect anomalies in time series data, wherein the time series data comprises a plurality of series of data points;
- cause the machine learning model to be trained to detect anomalies in the time series data that is to be received as input to the machine learning model;
- determine a number of computing resources to be used to apply the machine learning model to detect the anomalies in the time series data, based, at least in part, on:
- a first factor comprising a numerical value representing a minimum performance commitment for performing anomaly detection;
- a second factor comprising a numerical value representing a compute capacity for an individual one of the computing resources to perform detection of the anomalies in the time series data using the machine learning model;
- and
- a number of different series of data points in the plurality of series of data points to be evaluated by the machine learning model to detect the anomalies in the time series data, wherein the determined number of computing resources to be used is proportional to the number of different series of data points in the plurality of series of data points to be evaluated;
- provision the determined number of computing resources to be used to apply the machine learning model to the time series data;
- and
- enable the anomaly detection for the time series data using the provisioned number of computing resources by applying the trained machine learning model to the time series data, wherein the time series data is received as input to the machine learning model.
10 more elements
A method, comprising:
- receiving, by an anomaly detection system, a request to enable anomaly detection using a machine learning model to detect anomalies in time series data, wherein the time series data comprises a plurality of series of data points;
- training, by the anomaly detection system, the machine learning model to detect anomalies in the time series data that is to be received as input to the machine learning model;
- provisioning, by the anomaly detection system, a number of computing resources to be used to apply the machine learning model to detect the anomalies in the time series data, the number of computing resources being determined based, at least in part, on:
- a first factor comprising a numerical value representing a minimum performance commitment for performing anomaly detection;
- a second factor comprising a numerical value representing a compute capacity for an individual one of the computing resources to perform detection of the anomalies in the time series data using the machine learning model;
- and
- a number of different series of data points in the plurality of series of data points to be evaluated by the machine learning model to detect the anomalies in the time series data, wherein the determined number of computing resources to be used is proportional to the number of different series of data points in the plurality of series of data points to be evaluated;
- and
- enabling, by the anomaly detection system, the anomaly detection for the time series data using the provisioned number of computing resources by applying the trained machine learning model to the time series data, wherein the time series data is received as input to the machine learning model.
6 more elements
One or more non-transitory, computer-readable storage media, storing program instructions that when executed on or across one or more computing devices cause the one or more computing devices to implement:
- receiving a request to enable anomaly detection using a machine learning model to detect anomalies in time series data, wherein the time series data comprises a plurality of series of data points;
- causing the machine learning model to be trained to detect anomalies in the time series data that is to be received as input to the machine learning model;
- causing a number of computing resources to be provisioned to be used to apply the machine learning model to detect the anomalies in the time series data, the number of computing resources being determined based, at least in part, on:
- a first factor comprising a numerical value representing a minimum performance commitment for performing anomaly detection;
- a second factor comprising a numerical value representing a compute capacity for an individual one of the computing resources to perform detection of the anomalies in the time series data using the machine learning model;
- and
- a number of different series of data points in the plurality of series of data points to be evaluated by the machine learning model to detect the anomalies in the time series data, wherein the determined number of computing resources to be used is proportional to the number of different series of data points in the plurality of series of data points to be evaluated;
- and
- enabling the anomaly detection for the time series data using the provisioned number of computing resources by applying the trained machine learning model to the time series data, wherein the time series data is received as input to the machine learning model.
6 more elements
Dependencies are inferred from the claim text.