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Create/Update a Training Feature Log

Run Enrichment Notebook that will create/update a Lakehouse Table.

To use this activity within the API, use an ActivityCode of ML-FEATURE-LOG.

Example JSON​

An example of what the Task Config would look like for a task using this activity. Some of these variables would be set at the group level to avoid duplication between tasks.

NULL

Variable Reference​

The following variables are supported:

  • ModelName - (Required) Name of the ML Model.

  • FeatureEngineeringSchemaName - (Required) The schema name of the Feature Engineering table this log is derived from.

  • FeatureEngineeringTableName - (Required) The table name of the Feature Engineering table this log is derived from.

  • FeatureEngineeringIdColumnName - (Required) The identifier column in the Feature Engineering table.

  • DeltaSchemaName - (Required) The name of the Schema this Feature Log lives in.

  • DatabricksClusterId - (Optional) The Id of the Databricks Cluster to use to run the Notebook.

  • ExtractControlVariableName - (Optional) For incremental loads only, the name to assign the Extract Control variable in State Config for the ExtractControl value derived from the Extract Control Query above.

  • ExtractControlVariableSeedValue - (Optional) The initial value to set for the Extract Control variable in State Config - this will have no impact beyond the original seeding of the Extract Control variable in State Config.

  • SkipCreateVolumeAndSchema - (Optional) If a Schema and/or Volume has already been created, you can opt to skip this check - it will lead to better performance.

  • MaximumNumberOfAttemptsAllowed - (Optional) The total number of times the running of this Task can be attempted.

  • MinutesToWaitBeforeNextAttempt - (Optional) If a Task run fails, the number of minutes to wait before re-attempting the Task.

  • IsFederated - (Optional) Makes task available to other Insight Factories within this organisation.

  • Links - (Optional) NULL