- Machine Learning: other versions:
- What is Elastic Machine Learning?
- Setup and security
- Anomaly detection
- Finding anomalies
- Tutorial: Getting started with anomaly detection
- Advanced concepts
- API quick reference
- How-tos
- Generating alerts for anomaly detection jobs
- Aggregating data for faster performance
- Altering data in your datafeed with runtime fields
- Customizing detectors with custom rules
- Detecting anomalous categories of data
- Performing population analysis
- Reverting to a model snapshot
- Detecting anomalous locations in geographic data
- Mapping anomalies by location
- Adding custom URLs to machine learning results
- Anomaly detection jobs from visualizations
- Exporting and importing machine learning jobs
- Resources
- Data frame analytics
- Natural language processing
Limitations
editLimitations
editThe following limitations and known problems apply to the 8.15.5 release of the Elastic natural language processing trained models feature.
ELSER semantic search is limited to 512 tokens per field that inference is applied to
editWhen you use ELSER for semantic search, only the first 512 extracted tokens from each field of the ingested documents that ELSER is applied to are taken into account for the search process. If your data set contains long documents, divide them into smaller segments before ingestion if you need the full text to be searchable.
ELSER deployments don’t autoscale
editCurrently, ELSER deployments do not scale up and down automatically depending on the resource requirements of the ELSER processes. If you want to configure available resources for your ELSER deployments, you can manually set the number of allocations and threads per allocation by using the Trained Models UI in Kibana or the Update trained model deployment API.
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