- Machine Learning: other versions:
- Setup and security
- Getting started with machine learning
- Anomaly detection
- Overview
- Concepts
- Configure anomaly detection
- API quick reference
- Supplied configurations
- Function reference
- Examples
- Configuring anomaly detection alerts
- Aggregating data for faster performance
- Customizing detectors with custom rules
- Detecting anomalous categories of data
- Detecting anomalous locations in geographic data
- Performing population analysis
- Transforming data with script fields
- Adding custom URLs to machine learning results
- Handling delayed data
- Limitations
- Troubleshooting
- Data frame analytics
IMPORTANT: No additional bug fixes or documentation updates
will be released for this version. For the latest information, see the
current release documentation.
Data frame analytics
editData frame analytics
editUsing data frame analytics requires source data to be structured as a two dimensional "tabular" data structure, in other words a data frame. Transforms enable you to create data frames which can be used as the source for data frame analytics.
This functionality is in beta and is subject to change. The design and code is less mature than official GA features and is being provided as-is with no warranties. Beta features are not subject to the support SLA of official GA features.
Data frame analytics enable you to perform different analyses of your data and annotate it with the results. Consult Setup and security to learn more about the licence and the security privileges that are required to use data frame analytics.
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