Source: thenewstack.io
Training a ML Model to Forecast Kubernetes Node Anomalies
This is part of a series of contributed articles leading up to KubeCon + CloudNativeCon on Oct. 24-28. Using machine learning to forecast system anomalies and reduce alert noise are considered key domains to improve the performance of IT operations.
However, to increase the accuracy of machine learning, organizations need to collect proper data sets to train the machine learning model.
A TensorFlow machine learning engine was chosen to forecast the anomaly model, and the evaluation results are stored in MariaDB.
This means the metric pipeline from Prometheus to the anomaly forecast by the machine learning model should be completed in 30 seconds.
However, to increase the accuracy of machine learning, organizations need to collect proper data sets to train the machine learning model.
A TensorFlow machine learning engine was chosen to forecast the anomaly model, and the evaluation results are stored in MariaDB.
This means the metric pipeline from Prometheus to the anomaly forecast by the machine learning model should be completed in 30 seconds.
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