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Oracle Cloud Infrastructure v1.36.0 published on Thursday, May 16, 2024 by Pulumi

oci.AiAnomalyDetection.Model

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Oracle Cloud Infrastructure v1.36.0 published on Thursday, May 16, 2024 by Pulumi

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

    Creates a new Model.

    Example Usage

    import * as pulumi from "@pulumi/pulumi";
    import * as oci from "@pulumi/oci";
    
    const testModel = new oci.aianomalydetection.Model("test_model", {
        compartmentId: compartmentId,
        modelTrainingDetails: {
            dataAssetIds: modelModelTrainingDetailsDataAssetIds,
            algorithmHint: modelModelTrainingDetailsAlgorithmHint,
            targetFap: modelModelTrainingDetailsTargetFap,
            trainingFraction: modelModelTrainingDetailsTrainingFraction,
            windowSize: modelModelTrainingDetailsWindowSize,
        },
        projectId: testProject.id,
        definedTags: {
            "foo-namespace.bar-key": "value",
        },
        description: modelDescription,
        displayName: modelDisplayName,
        freeformTags: {
            "bar-key": "value",
        },
    });
    
    import pulumi
    import pulumi_oci as oci
    
    test_model = oci.ai_anomaly_detection.Model("test_model",
        compartment_id=compartment_id,
        model_training_details=oci.ai_anomaly_detection.ModelModelTrainingDetailsArgs(
            data_asset_ids=model_model_training_details_data_asset_ids,
            algorithm_hint=model_model_training_details_algorithm_hint,
            target_fap=model_model_training_details_target_fap,
            training_fraction=model_model_training_details_training_fraction,
            window_size=model_model_training_details_window_size,
        ),
        project_id=test_project["id"],
        defined_tags={
            "foo-namespace.bar-key": "value",
        },
        description=model_description,
        display_name=model_display_name,
        freeform_tags={
            "bar-key": "value",
        })
    
    package main
    
    import (
    	"github.com/pulumi/pulumi-oci/sdk/go/oci/AiAnomalyDetection"
    	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
    )
    
    func main() {
    	pulumi.Run(func(ctx *pulumi.Context) error {
    		_, err := AiAnomalyDetection.NewModel(ctx, "test_model", &AiAnomalyDetection.ModelArgs{
    			CompartmentId: pulumi.Any(compartmentId),
    			ModelTrainingDetails: &aianomalydetection.ModelModelTrainingDetailsArgs{
    				DataAssetIds:     pulumi.Any(modelModelTrainingDetailsDataAssetIds),
    				AlgorithmHint:    pulumi.Any(modelModelTrainingDetailsAlgorithmHint),
    				TargetFap:        pulumi.Any(modelModelTrainingDetailsTargetFap),
    				TrainingFraction: pulumi.Any(modelModelTrainingDetailsTrainingFraction),
    				WindowSize:       pulumi.Any(modelModelTrainingDetailsWindowSize),
    			},
    			ProjectId: pulumi.Any(testProject.Id),
    			DefinedTags: pulumi.Map{
    				"foo-namespace.bar-key": pulumi.Any("value"),
    			},
    			Description: pulumi.Any(modelDescription),
    			DisplayName: pulumi.Any(modelDisplayName),
    			FreeformTags: pulumi.Map{
    				"bar-key": pulumi.Any("value"),
    			},
    		})
    		if err != nil {
    			return err
    		}
    		return nil
    	})
    }
    
    using System.Collections.Generic;
    using System.Linq;
    using Pulumi;
    using Oci = Pulumi.Oci;
    
    return await Deployment.RunAsync(() => 
    {
        var testModel = new Oci.AiAnomalyDetection.Model("test_model", new()
        {
            CompartmentId = compartmentId,
            ModelTrainingDetails = new Oci.AiAnomalyDetection.Inputs.ModelModelTrainingDetailsArgs
            {
                DataAssetIds = modelModelTrainingDetailsDataAssetIds,
                AlgorithmHint = modelModelTrainingDetailsAlgorithmHint,
                TargetFap = modelModelTrainingDetailsTargetFap,
                TrainingFraction = modelModelTrainingDetailsTrainingFraction,
                WindowSize = modelModelTrainingDetailsWindowSize,
            },
            ProjectId = testProject.Id,
            DefinedTags = 
            {
                { "foo-namespace.bar-key", "value" },
            },
            Description = modelDescription,
            DisplayName = modelDisplayName,
            FreeformTags = 
            {
                { "bar-key", "value" },
            },
        });
    
    });
    
    package generated_program;
    
    import com.pulumi.Context;
    import com.pulumi.Pulumi;
    import com.pulumi.core.Output;
    import com.pulumi.oci.AiAnomalyDetection.Model;
    import com.pulumi.oci.AiAnomalyDetection.ModelArgs;
    import com.pulumi.oci.AiAnomalyDetection.inputs.ModelModelTrainingDetailsArgs;
    import java.util.List;
    import java.util.ArrayList;
    import java.util.Map;
    import java.io.File;
    import java.nio.file.Files;
    import java.nio.file.Paths;
    
    public class App {
        public static void main(String[] args) {
            Pulumi.run(App::stack);
        }
    
        public static void stack(Context ctx) {
            var testModel = new Model("testModel", ModelArgs.builder()        
                .compartmentId(compartmentId)
                .modelTrainingDetails(ModelModelTrainingDetailsArgs.builder()
                    .dataAssetIds(modelModelTrainingDetailsDataAssetIds)
                    .algorithmHint(modelModelTrainingDetailsAlgorithmHint)
                    .targetFap(modelModelTrainingDetailsTargetFap)
                    .trainingFraction(modelModelTrainingDetailsTrainingFraction)
                    .windowSize(modelModelTrainingDetailsWindowSize)
                    .build())
                .projectId(testProject.id())
                .definedTags(Map.of("foo-namespace.bar-key", "value"))
                .description(modelDescription)
                .displayName(modelDisplayName)
                .freeformTags(Map.of("bar-key", "value"))
                .build());
    
        }
    }
    
    resources:
      testModel:
        type: oci:AiAnomalyDetection:Model
        name: test_model
        properties:
          compartmentId: ${compartmentId}
          modelTrainingDetails:
            dataAssetIds: ${modelModelTrainingDetailsDataAssetIds}
            algorithmHint: ${modelModelTrainingDetailsAlgorithmHint}
            targetFap: ${modelModelTrainingDetailsTargetFap}
            trainingFraction: ${modelModelTrainingDetailsTrainingFraction}
            windowSize: ${modelModelTrainingDetailsWindowSize}
          projectId: ${testProject.id}
          definedTags:
            foo-namespace.bar-key: value
          description: ${modelDescription}
          displayName: ${modelDisplayName}
          freeformTags:
            bar-key: value
    

    Create Model Resource

    Resources are created with functions called constructors. To learn more about declaring and configuring resources, see Resources.

    Constructor syntax

    new Model(name: string, args: ModelArgs, opts?: CustomResourceOptions);
    @overload
    def Model(resource_name: str,
              args: ModelArgs,
              opts: Optional[ResourceOptions] = None)
    
    @overload
    def Model(resource_name: str,
              opts: Optional[ResourceOptions] = None,
              compartment_id: Optional[str] = None,
              model_training_details: Optional[_aianomalydetection.ModelModelTrainingDetailsArgs] = None,
              project_id: Optional[str] = None,
              defined_tags: Optional[Mapping[str, Any]] = None,
              description: Optional[str] = None,
              display_name: Optional[str] = None,
              freeform_tags: Optional[Mapping[str, Any]] = None)
    func NewModel(ctx *Context, name string, args ModelArgs, opts ...ResourceOption) (*Model, error)
    public Model(string name, ModelArgs args, CustomResourceOptions? opts = null)
    public Model(String name, ModelArgs args)
    public Model(String name, ModelArgs args, CustomResourceOptions options)
    
    type: oci:AiAnomalyDetection:Model
    properties: # The arguments to resource properties.
    options: # Bag of options to control resource's behavior.
    
    

    Parameters

    name string
    The unique name of the resource.
    args ModelArgs
    The arguments to resource properties.
    opts CustomResourceOptions
    Bag of options to control resource's behavior.
    resource_name str
    The unique name of the resource.
    args ModelArgs
    The arguments to resource properties.
    opts ResourceOptions
    Bag of options to control resource's behavior.
    ctx Context
    Context object for the current deployment.
    name string
    The unique name of the resource.
    args ModelArgs
    The arguments to resource properties.
    opts ResourceOption
    Bag of options to control resource's behavior.
    name string
    The unique name of the resource.
    args ModelArgs
    The arguments to resource properties.
    opts CustomResourceOptions
    Bag of options to control resource's behavior.
    name String
    The unique name of the resource.
    args ModelArgs
    The arguments to resource properties.
    options CustomResourceOptions
    Bag of options to control resource's behavior.

    Example

    The following reference example uses placeholder values for all input properties.

    var modelResource = new Oci.AiAnomalyDetection.Model("modelResource", new()
    {
        CompartmentId = "string",
        ModelTrainingDetails = new Oci.AiAnomalyDetection.Inputs.ModelModelTrainingDetailsArgs
        {
            DataAssetIds = new[]
            {
                "string",
            },
            AlgorithmHint = "string",
            TargetFap = 0,
            TrainingFraction = 0,
            WindowSize = 0,
        },
        ProjectId = "string",
        DefinedTags = 
        {
            { "string", "any" },
        },
        Description = "string",
        DisplayName = "string",
        FreeformTags = 
        {
            { "string", "any" },
        },
    });
    
    example, err := AiAnomalyDetection.NewModel(ctx, "modelResource", &AiAnomalyDetection.ModelArgs{
    	CompartmentId: pulumi.String("string"),
    	ModelTrainingDetails: &aianomalydetection.ModelModelTrainingDetailsArgs{
    		DataAssetIds: pulumi.StringArray{
    			pulumi.String("string"),
    		},
    		AlgorithmHint:    pulumi.String("string"),
    		TargetFap:        pulumi.Float64(0),
    		TrainingFraction: pulumi.Float64(0),
    		WindowSize:       pulumi.Int(0),
    	},
    	ProjectId: pulumi.String("string"),
    	DefinedTags: pulumi.Map{
    		"string": pulumi.Any("any"),
    	},
    	Description: pulumi.String("string"),
    	DisplayName: pulumi.String("string"),
    	FreeformTags: pulumi.Map{
    		"string": pulumi.Any("any"),
    	},
    })
    
    var modelResource = new Model("modelResource", ModelArgs.builder()        
        .compartmentId("string")
        .modelTrainingDetails(ModelModelTrainingDetailsArgs.builder()
            .dataAssetIds("string")
            .algorithmHint("string")
            .targetFap(0)
            .trainingFraction(0)
            .windowSize(0)
            .build())
        .projectId("string")
        .definedTags(Map.of("string", "any"))
        .description("string")
        .displayName("string")
        .freeformTags(Map.of("string", "any"))
        .build());
    
    model_resource = oci.ai_anomaly_detection.Model("modelResource",
        compartment_id="string",
        model_training_details=oci.ai_anomaly_detection.ModelModelTrainingDetailsArgs(
            data_asset_ids=["string"],
            algorithm_hint="string",
            target_fap=0,
            training_fraction=0,
            window_size=0,
        ),
        project_id="string",
        defined_tags={
            "string": "any",
        },
        description="string",
        display_name="string",
        freeform_tags={
            "string": "any",
        })
    
    const modelResource = new oci.aianomalydetection.Model("modelResource", {
        compartmentId: "string",
        modelTrainingDetails: {
            dataAssetIds: ["string"],
            algorithmHint: "string",
            targetFap: 0,
            trainingFraction: 0,
            windowSize: 0,
        },
        projectId: "string",
        definedTags: {
            string: "any",
        },
        description: "string",
        displayName: "string",
        freeformTags: {
            string: "any",
        },
    });
    
    type: oci:AiAnomalyDetection:Model
    properties:
        compartmentId: string
        definedTags:
            string: any
        description: string
        displayName: string
        freeformTags:
            string: any
        modelTrainingDetails:
            algorithmHint: string
            dataAssetIds:
                - string
            targetFap: 0
            trainingFraction: 0
            windowSize: 0
        projectId: string
    

    Model Resource Properties

    To learn more about resource properties and how to use them, see Inputs and Outputs in the Architecture and Concepts docs.

    Inputs

    The Model resource accepts the following input properties:

    CompartmentId string
    (Updatable) The OCID for the ai model's compartment.
    ModelTrainingDetails ModelModelTrainingDetails
    Specifies the details of the MSET model during the create call.
    ProjectId string

    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

    DefinedTags Dictionary<string, object>
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
    Description string
    (Updatable) A short description of the ai model.
    DisplayName string
    (Updatable) A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.
    FreeformTags Dictionary<string, object>
    (Updatable) Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
    CompartmentId string
    (Updatable) The OCID for the ai model's compartment.
    ModelTrainingDetails ModelModelTrainingDetailsArgs
    Specifies the details of the MSET model during the create call.
    ProjectId string

    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

    DefinedTags map[string]interface{}
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
    Description string
    (Updatable) A short description of the ai model.
    DisplayName string
    (Updatable) A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.
    FreeformTags map[string]interface{}
    (Updatable) Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
    compartmentId String
    (Updatable) The OCID for the ai model's compartment.
    modelTrainingDetails ModelModelTrainingDetails
    Specifies the details of the MSET model during the create call.
    projectId String

    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

    definedTags Map<String,Object>
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
    description String
    (Updatable) A short description of the ai model.
    displayName String
    (Updatable) A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.
    freeformTags Map<String,Object>
    (Updatable) Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
    compartmentId string
    (Updatable) The OCID for the ai model's compartment.
    modelTrainingDetails ModelModelTrainingDetails
    Specifies the details of the MSET model during the create call.
    projectId string

    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

    definedTags {[key: string]: any}
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
    description string
    (Updatable) A short description of the ai model.
    displayName string
    (Updatable) A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.
    freeformTags {[key: string]: any}
    (Updatable) Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
    compartment_id str
    (Updatable) The OCID for the ai model's compartment.
    model_training_details aianomalydetection.ModelModelTrainingDetailsArgs
    Specifies the details of the MSET model during the create call.
    project_id str

    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

    defined_tags Mapping[str, Any]
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
    description str
    (Updatable) A short description of the ai model.
    display_name str
    (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 Mapping[str, Any]
    (Updatable) Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
    compartmentId String
    (Updatable) The OCID for the ai model's compartment.
    modelTrainingDetails Property Map
    Specifies the details of the MSET model during the create call.
    projectId String

    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

    definedTags Map<Any>
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
    description String
    (Updatable) A short description of the ai model.
    displayName String
    (Updatable) A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.
    freeformTags Map<Any>
    (Updatable) Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}

    Outputs

    All input properties are implicitly available as output properties. Additionally, the Model resource produces the following output properties:

    Id string
    The provider-assigned unique ID for this managed resource.
    LifecycleDetails string
    A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
    ModelTrainingResults List<ModelModelTrainingResult>
    Specifies the details for an Anomaly Detection model trained with MSET.
    State string
    The state of the model.
    SystemTags Dictionary<string, object>
    Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
    TimeCreated string
    The time the the Model was created. An RFC3339 formatted datetime string.
    TimeUpdated string
    The time the Model was updated. An RFC3339 formatted datetime string.
    Id string
    The provider-assigned unique ID for this managed resource.
    LifecycleDetails string
    A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
    ModelTrainingResults []ModelModelTrainingResult
    Specifies the details for an Anomaly Detection model trained with MSET.
    State string
    The state of the model.
    SystemTags map[string]interface{}
    Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
    TimeCreated string
    The time the the Model was created. An RFC3339 formatted datetime string.
    TimeUpdated string
    The time the Model was updated. An RFC3339 formatted datetime string.
    id String
    The provider-assigned unique ID for this managed resource.
    lifecycleDetails String
    A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
    modelTrainingResults List<ModelModelTrainingResult>
    Specifies the details for an Anomaly Detection model trained with MSET.
    state String
    The state of the model.
    systemTags Map<String,Object>
    Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
    timeCreated String
    The time the the Model was created. An RFC3339 formatted datetime string.
    timeUpdated String
    The time the Model was updated. An RFC3339 formatted datetime string.
    id string
    The provider-assigned unique ID for this managed resource.
    lifecycleDetails string
    A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
    modelTrainingResults ModelModelTrainingResult[]
    Specifies the details for an Anomaly Detection model trained with MSET.
    state string
    The state of the model.
    systemTags {[key: string]: any}
    Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
    timeCreated string
    The time the the Model was created. An RFC3339 formatted datetime string.
    timeUpdated string
    The time the Model was updated. An RFC3339 formatted datetime string.
    id str
    The provider-assigned unique ID for this managed resource.
    lifecycle_details str
    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_results Sequence[aianomalydetection.ModelModelTrainingResult]
    Specifies the details for an Anomaly Detection model trained with MSET.
    state str
    The state of the model.
    system_tags Mapping[str, Any]
    Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
    time_created str
    The time the the Model was created. An RFC3339 formatted datetime string.
    time_updated str
    The time the Model was updated. An RFC3339 formatted datetime string.
    id String
    The provider-assigned unique ID for this managed resource.
    lifecycleDetails String
    A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
    modelTrainingResults List<Property Map>
    Specifies the details for an Anomaly Detection model trained with MSET.
    state String
    The state of the model.
    systemTags Map<Any>
    Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
    timeCreated String
    The time the the Model was created. An RFC3339 formatted datetime string.
    timeUpdated String
    The time the Model was updated. An RFC3339 formatted datetime string.

    Look up Existing Model Resource

    Get an existing Model resource’s state with the given name, ID, and optional extra properties used to qualify the lookup.

    public static get(name: string, id: Input<ID>, state?: ModelState, opts?: CustomResourceOptions): Model
    @staticmethod
    def get(resource_name: str,
            id: str,
            opts: Optional[ResourceOptions] = None,
            compartment_id: Optional[str] = None,
            defined_tags: Optional[Mapping[str, Any]] = None,
            description: Optional[str] = None,
            display_name: Optional[str] = None,
            freeform_tags: Optional[Mapping[str, Any]] = None,
            lifecycle_details: Optional[str] = None,
            model_training_details: Optional[_aianomalydetection.ModelModelTrainingDetailsArgs] = None,
            model_training_results: Optional[Sequence[_aianomalydetection.ModelModelTrainingResultArgs]] = None,
            project_id: Optional[str] = None,
            state: Optional[str] = None,
            system_tags: Optional[Mapping[str, Any]] = None,
            time_created: Optional[str] = None,
            time_updated: Optional[str] = None) -> Model
    func GetModel(ctx *Context, name string, id IDInput, state *ModelState, opts ...ResourceOption) (*Model, error)
    public static Model Get(string name, Input<string> id, ModelState? state, CustomResourceOptions? opts = null)
    public static Model get(String name, Output<String> id, ModelState state, CustomResourceOptions options)
    Resource lookup is not supported in YAML
    name
    The unique name of the resulting resource.
    id
    The unique provider ID of the resource to lookup.
    state
    Any extra arguments used during the lookup.
    opts
    A bag of options that control this resource's behavior.
    resource_name
    The unique name of the resulting resource.
    id
    The unique provider ID of the resource to lookup.
    name
    The unique name of the resulting resource.
    id
    The unique provider ID of the resource to lookup.
    state
    Any extra arguments used during the lookup.
    opts
    A bag of options that control this resource's behavior.
    name
    The unique name of the resulting resource.
    id
    The unique provider ID of the resource to lookup.
    state
    Any extra arguments used during the lookup.
    opts
    A bag of options that control this resource's behavior.
    name
    The unique name of the resulting resource.
    id
    The unique provider ID of the resource to lookup.
    state
    Any extra arguments used during the lookup.
    opts
    A bag of options that control this resource's behavior.
    The following state arguments are supported:
    CompartmentId string
    (Updatable) The OCID for the ai model's compartment.
    DefinedTags Dictionary<string, object>
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
    Description string
    (Updatable) A short description of the ai model.
    DisplayName string
    (Updatable) A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.
    FreeformTags Dictionary<string, object>
    (Updatable) Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
    LifecycleDetails string
    A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
    ModelTrainingDetails ModelModelTrainingDetails
    Specifies the details of the MSET model during the create call.
    ModelTrainingResults List<ModelModelTrainingResult>
    Specifies the details for an Anomaly Detection model trained with MSET.
    ProjectId string

    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

    State string
    The state of the model.
    SystemTags Dictionary<string, object>
    Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
    TimeCreated string
    The time the the Model was created. An RFC3339 formatted datetime string.
    TimeUpdated string
    The time the Model was updated. An RFC3339 formatted datetime string.
    CompartmentId string
    (Updatable) The OCID for the ai model's compartment.
    DefinedTags map[string]interface{}
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
    Description string
    (Updatable) A short description of the ai model.
    DisplayName string
    (Updatable) A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.
    FreeformTags map[string]interface{}
    (Updatable) Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
    LifecycleDetails string
    A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
    ModelTrainingDetails ModelModelTrainingDetailsArgs
    Specifies the details of the MSET model during the create call.
    ModelTrainingResults []ModelModelTrainingResultArgs
    Specifies the details for an Anomaly Detection model trained with MSET.
    ProjectId string

    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

    State string
    The state of the model.
    SystemTags map[string]interface{}
    Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
    TimeCreated string
    The time the the Model was created. An RFC3339 formatted datetime string.
    TimeUpdated string
    The time the Model was updated. An RFC3339 formatted datetime string.
    compartmentId String
    (Updatable) The OCID for the ai model's compartment.
    definedTags Map<String,Object>
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
    description String
    (Updatable) A short description of the ai model.
    displayName String
    (Updatable) A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.
    freeformTags Map<String,Object>
    (Updatable) Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
    lifecycleDetails String
    A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
    modelTrainingDetails ModelModelTrainingDetails
    Specifies the details of the MSET model during the create call.
    modelTrainingResults List<ModelModelTrainingResult>
    Specifies the details for an Anomaly Detection model trained with MSET.
    projectId String

    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

    state String
    The state of the model.
    systemTags Map<String,Object>
    Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
    timeCreated String
    The time the the Model was created. An RFC3339 formatted datetime string.
    timeUpdated String
    The time the Model was updated. An RFC3339 formatted datetime string.
    compartmentId string
    (Updatable) The OCID for the ai model's compartment.
    definedTags {[key: string]: any}
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
    description string
    (Updatable) A short description of the ai model.
    displayName string
    (Updatable) A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.
    freeformTags {[key: string]: any}
    (Updatable) Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
    lifecycleDetails string
    A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
    modelTrainingDetails ModelModelTrainingDetails
    Specifies the details of the MSET model during the create call.
    modelTrainingResults ModelModelTrainingResult[]
    Specifies the details for an Anomaly Detection model trained with MSET.
    projectId string

    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

    state string
    The state of the model.
    systemTags {[key: string]: any}
    Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
    timeCreated string
    The time the the Model was created. An RFC3339 formatted datetime string.
    timeUpdated string
    The time the Model was updated. An RFC3339 formatted datetime string.
    compartment_id str
    (Updatable) The OCID for the ai model's compartment.
    defined_tags Mapping[str, Any]
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
    description str
    (Updatable) A short description of the ai model.
    display_name str
    (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 Mapping[str, Any]
    (Updatable) Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
    lifecycle_details str
    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 aianomalydetection.ModelModelTrainingDetailsArgs
    Specifies the details of the MSET model during the create call.
    model_training_results Sequence[aianomalydetection.ModelModelTrainingResultArgs]
    Specifies the details for an Anomaly Detection model trained with MSET.
    project_id str

    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

    state str
    The state of the model.
    system_tags Mapping[str, Any]
    Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
    time_created str
    The time the the Model was created. An RFC3339 formatted datetime string.
    time_updated str
    The time the Model was updated. An RFC3339 formatted datetime string.
    compartmentId String
    (Updatable) The OCID for the ai model's compartment.
    definedTags Map<Any>
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
    description String
    (Updatable) A short description of the ai model.
    displayName String
    (Updatable) A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.
    freeformTags Map<Any>
    (Updatable) Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
    lifecycleDetails String
    A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
    modelTrainingDetails Property Map
    Specifies the details of the MSET model during the create call.
    modelTrainingResults List<Property Map>
    Specifies the details for an Anomaly Detection model trained with MSET.
    projectId String

    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

    state String
    The state of the model.
    systemTags Map<Any>
    Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
    timeCreated String
    The time the the Model was created. An RFC3339 formatted datetime string.
    timeUpdated String
    The time the Model was updated. An RFC3339 formatted datetime string.

    Supporting Types

    ModelModelTrainingDetails, ModelModelTrainingDetailsArgs

    DataAssetIds List<string>
    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.
    AlgorithmHint string
    User can choose specific algorithm for training.
    TargetFap double
    A target model accuracy metric user provides as their requirement
    TrainingFraction double
    Fraction of total data that is used for training the model. The remaining is used for validation of the model.
    WindowSize int
    This value would determine the window size of the training algorithm.
    DataAssetIds []string
    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.
    AlgorithmHint string
    User can choose specific algorithm for training.
    TargetFap float64
    A target model accuracy metric user provides as their requirement
    TrainingFraction float64
    Fraction of total data that is used for training the model. The remaining is used for validation of the model.
    WindowSize int
    This value would determine the window size of the training algorithm.
    dataAssetIds List<String>
    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.
    algorithmHint String
    User can choose specific algorithm for training.
    targetFap Double
    A target model accuracy metric user provides as their requirement
    trainingFraction Double
    Fraction of total data that is used for training the model. The remaining is used for validation of the model.
    windowSize Integer
    This value would determine the window size of the training algorithm.
    dataAssetIds string[]
    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.
    algorithmHint string
    User can choose specific algorithm for training.
    targetFap number
    A target model accuracy metric user provides as their requirement
    trainingFraction number
    Fraction of total data that is used for training the model. The remaining is used for validation of the model.
    windowSize number
    This value would determine the window size of the training algorithm.
    data_asset_ids Sequence[str]
    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.
    algorithm_hint str
    User can choose specific algorithm for training.
    target_fap float
    A target model accuracy metric user provides as their requirement
    training_fraction float
    Fraction of total data that is used for training the model. The remaining is used for validation of the model.
    window_size int
    This value would determine the window size of the training algorithm.
    dataAssetIds List<String>
    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.
    algorithmHint String
    User can choose specific algorithm for training.
    targetFap Number
    A target model accuracy metric user provides as their requirement
    trainingFraction Number
    Fraction of total data that is used for training the model. The remaining is used for validation of the model.
    windowSize Number
    This value would determine the window size of the training algorithm.

    ModelModelTrainingResult, ModelModelTrainingResultArgs

    Fap double
    Accuracy metric for a signal.
    IsTrainingGoalAchieved bool
    A boolean value to indicate if train goal/targetFap is achieved for trained model
    Mae double
    MaxInferenceSyncRows int
    MultivariateFap double
    The model accuracy metric on timestamp level.
    Rmse double
    RowReductionDetails List<ModelModelTrainingResultRowReductionDetail>
    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.
    SignalDetails List<ModelModelTrainingResultSignalDetail>
    The list of signal details.
    Warning string
    A warning message to explain the reason when targetFap cannot be achieved for trained model
    WindowSize int
    This value would determine the window size of the training algorithm.
    Fap float64
    Accuracy metric for a signal.
    IsTrainingGoalAchieved bool
    A boolean value to indicate if train goal/targetFap is achieved for trained model
    Mae float64
    MaxInferenceSyncRows int
    MultivariateFap float64
    The model accuracy metric on timestamp level.
    Rmse float64
    RowReductionDetails []ModelModelTrainingResultRowReductionDetail
    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.
    SignalDetails []ModelModelTrainingResultSignalDetail
    The list of signal details.
    Warning string
    A warning message to explain the reason when targetFap cannot be achieved for trained model
    WindowSize int
    This value would determine the window size of the training algorithm.
    fap Double
    Accuracy metric for a signal.
    isTrainingGoalAchieved Boolean
    A boolean value to indicate if train goal/targetFap is achieved for trained model
    mae Double
    maxInferenceSyncRows Integer
    multivariateFap Double
    The model accuracy metric on timestamp level.
    rmse Double
    rowReductionDetails List<ModelModelTrainingResultRowReductionDetail>
    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.
    signalDetails List<ModelModelTrainingResultSignalDetail>
    The list of signal details.
    warning String
    A warning message to explain the reason when targetFap cannot be achieved for trained model
    windowSize Integer
    This value would determine the window size of the training algorithm.
    fap number
    Accuracy metric for a signal.
    isTrainingGoalAchieved boolean
    A boolean value to indicate if train goal/targetFap is achieved for trained model
    mae number
    maxInferenceSyncRows number
    multivariateFap number
    The model accuracy metric on timestamp level.
    rmse number
    rowReductionDetails ModelModelTrainingResultRowReductionDetail[]
    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.
    signalDetails ModelModelTrainingResultSignalDetail[]
    The list of signal details.
    warning string
    A warning message to explain the reason when targetFap cannot be achieved for trained model
    windowSize number
    This value would determine the window size of the training algorithm.
    fap float
    Accuracy metric for a signal.
    is_training_goal_achieved bool
    A boolean value to indicate if train goal/targetFap is achieved for trained model
    mae float
    max_inference_sync_rows int
    multivariate_fap float
    The model accuracy metric on timestamp level.
    rmse float
    row_reduction_details Sequence[aianomalydetection.ModelModelTrainingResultRowReductionDetail]
    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.
    signal_details Sequence[aianomalydetection.ModelModelTrainingResultSignalDetail]
    The list of signal details.
    warning str
    A warning message to explain the reason when targetFap cannot be achieved for trained model
    window_size int
    This value would determine the window size of the training algorithm.
    fap Number
    Accuracy metric for a signal.
    isTrainingGoalAchieved Boolean
    A boolean value to indicate if train goal/targetFap is achieved for trained model
    mae Number
    maxInferenceSyncRows Number
    multivariateFap Number
    The model accuracy metric on timestamp level.
    rmse Number
    rowReductionDetails List<Property Map>
    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.
    signalDetails List<Property Map>
    The list of signal details.
    warning String
    A warning message to explain the reason when targetFap cannot be achieved for trained model
    windowSize Number
    This value would determine the window size of the training algorithm.

    ModelModelTrainingResultRowReductionDetail, ModelModelTrainingResultRowReductionDetailArgs

    IsReductionEnabled bool
    A boolean value to indicate if row reduction is applied
    ReductionMethod string
    Method for row reduction:

    • DELETE_ROW - delete rows with equal intervals
    • AVERAGE_ROW - average multiple rows to one row
    ReductionPercentage double
    A percentage to reduce data size down to on top of original data
    IsReductionEnabled bool
    A boolean value to indicate if row reduction is applied
    ReductionMethod string
    Method for row reduction:

    • DELETE_ROW - delete rows with equal intervals
    • AVERAGE_ROW - average multiple rows to one row
    ReductionPercentage float64
    A percentage to reduce data size down to on top of original data
    isReductionEnabled Boolean
    A boolean value to indicate if row reduction is applied
    reductionMethod String
    Method for row reduction:

    • DELETE_ROW - delete rows with equal intervals
    • AVERAGE_ROW - average multiple rows to one row
    reductionPercentage Double
    A percentage to reduce data size down to on top of original data
    isReductionEnabled boolean
    A boolean value to indicate if row reduction is applied
    reductionMethod string
    Method for row reduction:

    • DELETE_ROW - delete rows with equal intervals
    • AVERAGE_ROW - average multiple rows to one row
    reductionPercentage number
    A percentage to reduce data size down to on top of original data
    is_reduction_enabled bool
    A boolean value to indicate if row reduction is applied
    reduction_method str
    Method for row reduction:

    • DELETE_ROW - delete rows with equal intervals
    • AVERAGE_ROW - average multiple rows to one row
    reduction_percentage float
    A percentage to reduce data size down to on top of original data
    isReductionEnabled Boolean
    A boolean value to indicate if row reduction is applied
    reductionMethod String
    Method for row reduction:

    • DELETE_ROW - delete rows with equal intervals
    • AVERAGE_ROW - average multiple rows to one row
    reductionPercentage Number
    A percentage to reduce data size down to on top of original data

    ModelModelTrainingResultSignalDetail, ModelModelTrainingResultSignalDetailArgs

    Details string
    detailed information for a signal.
    Fap double
    Accuracy metric for a signal.
    IsQuantized bool
    A boolean value to indicate if a signal is quantized or not.
    Max double
    Max value within a signal.
    Min double
    Min value within a signal.
    MviRatio double
    The ratio of missing values in a signal filled/imputed by the IDP algorithm.
    SignalName string
    The name of a signal.
    Status string
    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 double
    Standard deviation of values within a signal.
    Details string
    detailed information for a signal.
    Fap float64
    Accuracy metric for a signal.
    IsQuantized bool
    A boolean value to indicate if a signal is quantized or not.
    Max float64
    Max value within a signal.
    Min float64
    Min value within a signal.
    MviRatio float64
    The ratio of missing values in a signal filled/imputed by the IDP algorithm.
    SignalName string
    The name of a signal.
    Status string
    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 float64
    Standard deviation of values within a signal.
    details String
    detailed information for a signal.
    fap Double
    Accuracy metric for a signal.
    isQuantized Boolean
    A boolean value to indicate if a signal is quantized or not.
    max Double
    Max value within a signal.
    min Double
    Min value within a signal.
    mviRatio Double
    The ratio of missing values in a signal filled/imputed by the IDP algorithm.
    signalName String
    The name of a signal.
    status String
    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 Double
    Standard deviation of values within a signal.
    details string
    detailed information for a signal.
    fap number
    Accuracy metric for a signal.
    isQuantized boolean
    A boolean value to indicate if a signal is quantized or not.
    max number
    Max value within a signal.
    min number
    Min value within a signal.
    mviRatio number
    The ratio of missing values in a signal filled/imputed by the IDP algorithm.
    signalName string
    The name of a signal.
    status string
    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 number
    Standard deviation of values within a signal.
    details str
    detailed information for a signal.
    fap float
    Accuracy metric for a signal.
    is_quantized bool
    A boolean value to indicate if a signal is quantized or not.
    max float
    Max value within a signal.
    min float
    Min value within a signal.
    mvi_ratio float
    The ratio of missing values in a signal filled/imputed by the IDP algorithm.
    signal_name str
    The name of a signal.
    status str
    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 float
    Standard deviation of values within a signal.
    details String
    detailed information for a signal.
    fap Number
    Accuracy metric for a signal.
    isQuantized Boolean
    A boolean value to indicate if a signal is quantized or not.
    max Number
    Max value within a signal.
    min Number
    Min value within a signal.
    mviRatio Number
    The ratio of missing values in a signal filled/imputed by the IDP algorithm.
    signalName String
    The name of a signal.
    status String
    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 Number
    Standard deviation of values within a signal.

    Import

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

    $ pulumi import oci:AiAnomalyDetection/model:Model test_model "id"
    

    To learn more about importing existing cloud resources, see Importing resources.

    Package Details

    Repository
    oci pulumi/pulumi-oci
    License
    Apache-2.0
    Notes
    This Pulumi package is based on the oci Terraform Provider.
    oci logo
    Oracle Cloud Infrastructure v1.36.0 published on Thursday, May 16, 2024 by Pulumi