mlflow
Use this command to manually train the machine learning models, exclude assets, and validate artifacts.
For more information about the Machine Learning Anomaly Detection feature, see the FortiAnalyzer Administration Guide.
Syntax
execute mlflow cancel <artifact_name>
execute mlflow delete <artifact_name>
execute mlflow deploy <artifact_name>
execute mlflow exclusion-list {artifact | model} {add | list | remove} <asset>
execute mlflow test <artifact_name> <start_time> <end_time>
execute mlflow train <model_type> <start_time> <end_time> [train-assets-limit]
execute mlflow undeploy <artifact_name>
|
Variable |
Description |
|---|---|
|
cancel <artifact_name> |
Cancel artifact training. |
|
delete <artifact_name> |
Permanently delete artifact. |
|
deploy <artifact_name> |
Deploy artifact for active inference. |
|
exclusion-list {artifact | model} {add | list | remove} <asset> |
Manage ML exclusion-lists.
|
|
test <artifact_name> <start_time> <end_time> |
Test/validate artifact on specified time range.
|
|
train <model_type> <start_time> <end_time> [train-assets-limit] |
Train the machine learning model on specified time range.
Example: execute mlflow train login-anomaly '2026-02-05' '2026-05-05' |
|
undeploy <artifact_name> |
Undeploy artifact from active inference. |