- X-Pack Reference for 6.0-6.2 and 5.x:
- Introduction
- Installing X-Pack
- Migrating to X-Pack
- Breaking Changes
- Securing Elasticsearch and Kibana
- Monitoring the Elastic Stack
- Alerting on Cluster and Index Events
- Reporting from Kibana
- Graphing Connections in Your Data
- Profiling your Queries and Aggregations
- Machine Learning in the Elastic Stack
- X-Pack Settings
- X-Pack APIs
- Info API
- Security APIs
- Watcher APIs
- Graph APIs
- Machine Learning APIs
- Close Jobs
- Create Datafeeds
- Create Jobs
- Delete Datafeeds
- Delete Jobs
- Delete Model Snapshots
- Flush Jobs
- Get Buckets
- Get Categories
- Get Datafeeds
- Get Datafeed Statistics
- Get Influencers
- Get Jobs
- Get Job Statistics
- Get Model Snapshots
- Get Records
- Open Jobs
- Post Data to Jobs
- Preview Datafeeds
- Revert Model Snapshots
- Start Datafeeds
- Stop Datafeeds
- Update Datafeeds
- Update Jobs
- Update Model Snapshots
- Validate Detectors
- Validate Jobs
- Definitions
- Troubleshooting
- Limitations
- License Management
- Release Notes
WARNING: Version 5.4 of the Elastic Stack has passed its EOL date.
This documentation is no longer being maintained and may be removed. If you are running this version, we strongly advise you to upgrade. For the latest information, see the current release documentation.
Machine Learning in the Elastic Stack
editMachine Learning in the Elastic Stack
editThe X-Pack machine learning features automate the analysis of time-series data by creating accurate baselines of normal behaviors in the data and identifying anomalous patterns in that data.
Using proprietary machine learning algorithms, the following circumstances are detected, scored, and linked with statistically significant influencers in the data:
- Anomalies related to temporal deviations in values, counts, or frequencies
- Statistical rarity
- Unusual behaviors for a member of a population
Automated periodicity detection and quick adaptation to changing data ensure that you don’t need to specify algorithms, models, or other data science-related configurations in order to get the benefits of machine learning.
Integration with the Elastic Stack
editMachine learning is tightly integrated with the Elastic Stack. Data is pulled from Elasticsearch for analysis and anomaly results are displayed in Kibana dashboards.
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