oci_ai_anomaly_detection_model

This resource provides the Model resource in Oracle Cloud Infrastructure Ai Anomaly Detection service.

Creates a new Model.

Example Usage

resource "oci_ai_anomaly_detection_model" "test_model" {
	#Required
	compartment_id = var.compartment_id
	model_training_details {
		#Required
		data_asset_ids = var.model_model_training_details_data_asset_ids

		#Optional
		algorithm_hint = var.model_model_training_details_algorithm_hint
		target_fap = var.model_model_training_details_target_fap
		training_fraction = var.model_model_training_details_training_fraction
		window_size = var.model_model_training_details_window_size
	}
	project_id = oci_ai_anomaly_detection_project.test_project.id

	#Optional
	defined_tags = {"foo-namespace.bar-key"= "value"}
	description = var.model_description
	display_name = var.model_display_name
	freeform_tags = {"bar-key"= "value"}
}

Argument Reference

The following arguments are supported:

• compartment_id - (Required) (Updatable) The OCID for the ai model’s compartment.
• defined_tags - (Optional) (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
• description - (Optional) (Updatable) A short description of the ai model.
• display_name - (Optional) (Updatable) A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.
• freeform_tags - (Optional) (Updatable) Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
• model_training_details - (Required) Specifies the details of the MSET model during the create call.
  • algorithm_hint - (Optional) User can choose specific algorithm for training.
  • data_asset_ids - (Required) The list of OCIDs of the data assets to train the model. The dataAssets have to be in the same project where the ai model would reside.
  • target_fap - (Optional) A target model accuracy metric user provides as their requirement
  • training_fraction - (Optional) Fraction of total data that is used for training the model. The remaining is used for validation of the model.
  • window_size - (Optional) This value would determine the window size of the training algorithm.
• project_id - (Required) The OCID of the project to associate with the model.

** IMPORTANT ** Any change to a property that does not support update will force the destruction and recreation of the resource with the new property values

Attributes Reference

The following attributes are exported:

• compartment_id - The OCID for the model’s compartment.
• defined_tags - Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
• description - A short description of the Model.
• display_name - A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.
• freeform_tags - Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
• id - The OCID of the model that is immutable on creation.
• lifecycle_details - A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
• model_training_details - Specifies the details of the MSET model during the create call.
  • algorithm_hint - User can choose specific algorithm for training.
  • data_asset_ids - The list of OCIDs of the data assets to train the model. The dataAssets have to be in the same project where the ai model would reside.
  • target_fap - A target model accuracy metric user provides as their requirement
  • training_fraction - Fraction of total data that is used for training the model. The remaining is used for validation of the model.
  • window_size - This value would determine the window size of the training algorithm.
• model_training_results - Specifies the details for an Anomaly Detection model trained with MSET.
  • algorithm - Actual algorithm used to train the model
  • fap - The final-achieved model accuracy metric on individual value level
  • is_training_goal_achieved - A boolean value to indicate if train goal/targetFap is achieved for trained model
  • multivariate_fap - The model accuracy metric on timestamp level.
  • row_reduction_details - Information regarding how/what row reduction methods will be applied. If this property is not present or is null, then it means row reduction is not applied.
    • is_reduction_enabled - A boolean value to indicate if row reduction is applied
    • reduction_method - Method for row reduction:
      • DELETE_ROW - delete rows with equal intervals
      • AVERAGE_ROW - average multiple rows to one row
    • reduction_percentage - A percentage to reduce data size down to on top of original data
  • signal_details - The list of signal details.
    • details - detailed information for a signal.
    • fap - Accuracy metric for a signal.
    • is_quantized - A boolean value to indicate if a signal is quantized or not.
    • max - Max value within a signal.
    • min - Min value within a signal.
    • mvi_ratio - The ratio of missing values in a signal filled/imputed by the IDP algorithm.
    • signal_name - The name of a signal.
    • status - Status of the signal:
      • ACCEPTED - the signal is used for training the model
      • DROPPED - the signal does not meet requirement, and is dropped before training the model.
      • OTHER - placeholder for other status
    • std - Standard deviation of values within a signal.
  • warning - A warning message to explain the reason when targetFap cannot be achieved for trained model
  • window_size - Window size defined during training or deduced by the algorithm.
• project_id - The OCID of the project to associate with the model.
• state - The state of the model.
• system_tags - Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
• time_created - The time the the Model was created. An RFC3339 formatted datetime string.
• time_updated - The time the Model was updated. An RFC3339 formatted datetime string.

Timeouts

The timeouts block allows you to specify timeouts for certain operations: * create - (Defaults to 20 minutes), when creating the Model * update - (Defaults to 20 minutes), when updating the Model * delete - (Defaults to 20 minutes), when destroying the Model

Import

Models can be imported using the id, e.g.

$ terraform import oci_ai_anomaly_detection_model.test_model "id"