AWS iot-sitewise documentation change
Summary
Fixed multiple instances of 'anomlay' typo to 'anomaly'
Security assessment
Typo corrections with no security impact.
Diff
diff --git a/iot-sitewise/latest/userguide/sitewise-anomaly-detection.md b/iot-sitewise/latest/userguide/sitewise-anomaly-detection.md index ad8bc116f..fafe95584 100644 --- a//iot-sitewise/latest/userguide/sitewise-anomaly-detection.md +++ b//iot-sitewise/latest/userguide/sitewise-anomaly-detection.md @@ -9 +9 @@ AWS IoT SiteWise native anomaly detection is a machine learning (ML) feature for -AWS IoT SiteWise native anomlay detection doesn’t require extensive ML knowledge or experience. You simply select the properties to train a custom ML model that finds potential failures for you. AWS IoT SiteWise native anomlay detection automatically creates the best model to learn your equipment’s normal operating conditions. The model is optimized to find abnormal equipment behavior that occurred in the historical data. Using either the AWS IoT SiteWise console or the SDK, you run the model to process new time-series data according to your desired schedule. +AWS IoT SiteWise native anomaly detection doesn’t require extensive ML knowledge or experience. You simply select the properties to train a custom ML model that finds potential failures for you. AWS IoT SiteWise native anomaly detection automatically creates the best model to learn your equipment’s normal operating conditions. The model is optimized to find abnormal equipment behavior that occurred in the historical data. Using either the AWS IoT SiteWise console or the SDK, you run the model to process new time-series data according to your desired schedule. @@ -17 +17 @@ To use AWS IoT SiteWise native anomaly detection, you do the following: - * Train your ML model using AWS IoT SiteWise native anomlay detection. + * Train your ML model using AWS IoT SiteWise native anomaly detection.