Explore the Anomaly Detection service within the Oracle Cloud AI service group to build your ML model to detect anomalies in production with a few simple steps.
Workshop length: 2 hours
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About This Workshop
Anomaly Detection is the identification of rare items, events, or observations in data that differ significantly from the expectation. This can be used for several scenarios like asset monitoring, maintenance and prognostic surveillance in industries such as utility, aviation and manufacturing.
The core of the Anomaly Detection Service at OCI is built on the MSET algorithm, which is a multivariate anomaly detection algorithm originally developed by Oracle Labs and patented at Oracle and had been successfully used in several industries for prognosis analysis.
The Anomaly Detection Service will create customized Machine Learning models, by taking the data uploaded by users, using MSET to train the model, and deploying the model into the cloud environment to be ready for detection. Users can then send new data to the detection endpoints to get the detected anomaly results.
In this workshop, we want to help users achieve the following objectives:
Understand a high level overview of the OCI Anomaly Detection Service
Learn how to train the ML models for multivariate anomaly detection using our OCI ADS
Learn how to do basic data transformation and processing to prepare raw data for model training with OCI ADS
Learn how to explain the model training results and iteratively training new models
Learn how to deploy the trained model into production for future anomaly detection
(Optional) Learn to use our OCI CLI/SDK to integrate the whole pipeline of using AD services
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