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Grafana v0.16.1 published on Saturday, Mar 15, 2025 by pulumiverse
pulumiverse/pulumi-grafana

grafana.MachineLearningOutlierDetector

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Grafana v0.16.1 published on Saturday, Mar 15, 2025 by pulumiverse
pulumiverse/pulumi-grafana
    Deprecated: grafana.index/machinelearningoutlierdetector.MachineLearningOutlierDetector has been deprecated in favor of grafana.machinelearning/outlierdetector.OutlierDetector

    An outlier detector monitors the results of a query and reports when its values are outside normal bands.

    The normal band is configured by choice of algorithm, its sensitivity and other configuration.

    Visit https://grafana.com/docs/grafana-cloud/machine-learning/outlier-detection/ for more details.

    Example Usage

    DBSCAN Outlier Detector

    This outlier detector uses the DBSCAN algorithm to detect outliers.

    import * as pulumi from "@pulumi/pulumi";
import * as grafana from "@pulumiverse/grafana";

const myDbscanOutlierDetector = new grafana.machinelearning.OutlierDetector("my_dbscan_outlier_detector", {
    name: "My DBSCAN outlier detector",
    description: "My DBSCAN Outlier Detector",
    metric: "tf_test_dbscan_job",
    datasourceType: "prometheus",
    datasourceUid: "AbCd12345",
    queryParams: {
        expr: "grafanacloud_grafana_instance_active_user_count",
    },
    interval: 300,
    algorithm: {
        name: "dbscan",
        sensitivity: 0.5,
        config: {
            epsilon: 1,
        },
    },
});

    import pulumi
import pulumiverse_grafana as grafana

my_dbscan_outlier_detector = grafana.machine_learning.OutlierDetector("my_dbscan_outlier_detector",
    name="My DBSCAN outlier detector",
    description="My DBSCAN Outlier Detector",
    metric="tf_test_dbscan_job",
    datasource_type="prometheus",
    datasource_uid="AbCd12345",
    query_params={
        "expr": "grafanacloud_grafana_instance_active_user_count",
    },
    interval=300,
    algorithm={
        "name": "dbscan",
        "sensitivity": 0.5,
        "config": {
            "epsilon": 1,
        },
    })

    package main

import (
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
	"github.com/pulumiverse/pulumi-grafana/sdk/go/grafana/machinelearning"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		_, err := machinelearning.NewOutlierDetector(ctx, "my_dbscan_outlier_detector", &machinelearning.OutlierDetectorArgs{
			Name:           pulumi.String("My DBSCAN outlier detector"),
			Description:    pulumi.String("My DBSCAN Outlier Detector"),
			Metric:         pulumi.String("tf_test_dbscan_job"),
			DatasourceType: pulumi.String("prometheus"),
			DatasourceUid:  pulumi.String("AbCd12345"),
			QueryParams: pulumi.StringMap{
				"expr": pulumi.String("grafanacloud_grafana_instance_active_user_count"),
			},
			Interval: pulumi.Int(300),
			Algorithm: &machinelearning.OutlierDetectorAlgorithmArgs{
				Name:        pulumi.String("dbscan"),
				Sensitivity: pulumi.Float64(0.5),
				Config: &machinelearning.OutlierDetectorAlgorithmConfigArgs{
					Epsilon: pulumi.Float64(1),
				},
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

    using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Grafana = Pulumiverse.Grafana;

return await Deployment.RunAsync(() => 
{
    var myDbscanOutlierDetector = new Grafana.MachineLearning.OutlierDetector("my_dbscan_outlier_detector", new()
    {
        Name = "My DBSCAN outlier detector",
        Description = "My DBSCAN Outlier Detector",
        Metric = "tf_test_dbscan_job",
        DatasourceType = "prometheus",
        DatasourceUid = "AbCd12345",
        QueryParams = 
        {
            { "expr", "grafanacloud_grafana_instance_active_user_count" },
        },
        Interval = 300,
        Algorithm = new Grafana.MachineLearning.Inputs.OutlierDetectorAlgorithmArgs
        {
            Name = "dbscan",
            Sensitivity = 0.5,
            Config = new Grafana.MachineLearning.Inputs.OutlierDetectorAlgorithmConfigArgs
            {
                Epsilon = 1,
            },
        },
    });

});

    package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.grafana.machineLearning.OutlierDetector;
import com.pulumi.grafana.machineLearning.OutlierDetectorArgs;
import com.pulumi.grafana.machineLearning.inputs.OutlierDetectorAlgorithmArgs;
import com.pulumi.grafana.machineLearning.inputs.OutlierDetectorAlgorithmConfigArgs;
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 myDbscanOutlierDetector = new OutlierDetector("myDbscanOutlierDetector", OutlierDetectorArgs.builder()
            .name("My DBSCAN outlier detector")
            .description("My DBSCAN Outlier Detector")
            .metric("tf_test_dbscan_job")
            .datasourceType("prometheus")
            .datasourceUid("AbCd12345")
            .queryParams(Map.of("expr", "grafanacloud_grafana_instance_active_user_count"))
            .interval(300)
            .algorithm(OutlierDetectorAlgorithmArgs.builder()
                .name("dbscan")
                .sensitivity(0.5)
                .config(OutlierDetectorAlgorithmConfigArgs.builder()
                    .epsilon(1)
                    .build())
                .build())
            .build());

    }
}

    resources:
  myDbscanOutlierDetector:
    type: grafana:machineLearning:OutlierDetector
    name: my_dbscan_outlier_detector
    properties:
      name: My DBSCAN outlier detector
      description: My DBSCAN Outlier Detector
      metric: tf_test_dbscan_job
      datasourceType: prometheus
      datasourceUid: AbCd12345
      queryParams:
        expr: grafanacloud_grafana_instance_active_user_count
      interval: 300
      algorithm:
        name: dbscan
        sensitivity: 0.5
        config:
          epsilon: 1

    MAD Outlier Detector

    This outlier detector uses the Median Absolute Deviation (MAD) algorithm to detect outliers.

    import * as pulumi from "@pulumi/pulumi";
import * as grafana from "@pulumiverse/grafana";

const myMadOutlierDetector = new grafana.machinelearning.OutlierDetector("my_mad_outlier_detector", {
    name: "My MAD outlier detector",
    description: "My MAD Outlier Detector",
    metric: "tf_test_mad_job",
    datasourceType: "prometheus",
    datasourceUid: "AbCd12345",
    queryParams: {
        expr: "grafanacloud_grafana_instance_active_user_count",
    },
    interval: 300,
    algorithm: {
        name: "mad",
        sensitivity: 0.7,
    },
});

    import pulumi
import pulumiverse_grafana as grafana

my_mad_outlier_detector = grafana.machine_learning.OutlierDetector("my_mad_outlier_detector",
    name="My MAD outlier detector",
    description="My MAD Outlier Detector",
    metric="tf_test_mad_job",
    datasource_type="prometheus",
    datasource_uid="AbCd12345",
    query_params={
        "expr": "grafanacloud_grafana_instance_active_user_count",
    },
    interval=300,
    algorithm={
        "name": "mad",
        "sensitivity": 0.7,
    })

    package main

import (
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
	"github.com/pulumiverse/pulumi-grafana/sdk/go/grafana/machinelearning"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		_, err := machinelearning.NewOutlierDetector(ctx, "my_mad_outlier_detector", &machinelearning.OutlierDetectorArgs{
			Name:           pulumi.String("My MAD outlier detector"),
			Description:    pulumi.String("My MAD Outlier Detector"),
			Metric:         pulumi.String("tf_test_mad_job"),
			DatasourceType: pulumi.String("prometheus"),
			DatasourceUid:  pulumi.String("AbCd12345"),
			QueryParams: pulumi.StringMap{
				"expr": pulumi.String("grafanacloud_grafana_instance_active_user_count"),
			},
			Interval: pulumi.Int(300),
			Algorithm: &machinelearning.OutlierDetectorAlgorithmArgs{
				Name:        pulumi.String("mad"),
				Sensitivity: pulumi.Float64(0.7),
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

    using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Grafana = Pulumiverse.Grafana;

return await Deployment.RunAsync(() => 
{
    var myMadOutlierDetector = new Grafana.MachineLearning.OutlierDetector("my_mad_outlier_detector", new()
    {
        Name = "My MAD outlier detector",
        Description = "My MAD Outlier Detector",
        Metric = "tf_test_mad_job",
        DatasourceType = "prometheus",
        DatasourceUid = "AbCd12345",
        QueryParams = 
        {
            { "expr", "grafanacloud_grafana_instance_active_user_count" },
        },
        Interval = 300,
        Algorithm = new Grafana.MachineLearning.Inputs.OutlierDetectorAlgorithmArgs
        {
            Name = "mad",
            Sensitivity = 0.7,
        },
    });

});

    package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.grafana.machineLearning.OutlierDetector;
import com.pulumi.grafana.machineLearning.OutlierDetectorArgs;
import com.pulumi.grafana.machineLearning.inputs.OutlierDetectorAlgorithmArgs;
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 myMadOutlierDetector = new OutlierDetector("myMadOutlierDetector", OutlierDetectorArgs.builder()
            .name("My MAD outlier detector")
            .description("My MAD Outlier Detector")
            .metric("tf_test_mad_job")
            .datasourceType("prometheus")
            .datasourceUid("AbCd12345")
            .queryParams(Map.of("expr", "grafanacloud_grafana_instance_active_user_count"))
            .interval(300)
            .algorithm(OutlierDetectorAlgorithmArgs.builder()
                .name("mad")
                .sensitivity(0.7)
                .build())
            .build());

    }
}

    resources:
  myMadOutlierDetector:
    type: grafana:machineLearning:OutlierDetector
    name: my_mad_outlier_detector
    properties:
      name: My MAD outlier detector
      description: My MAD Outlier Detector
      metric: tf_test_mad_job
      datasourceType: prometheus
      datasourceUid: AbCd12345
      queryParams:
        expr: grafanacloud_grafana_instance_active_user_count
      interval: 300
      algorithm:
        name: mad
        sensitivity: 0.7

    Create MachineLearningOutlierDetector Resource

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

    Constructor syntax

    new MachineLearningOutlierDetector(name: string, args: MachineLearningOutlierDetectorArgs, opts?: CustomResourceOptions);
    @overload
def MachineLearningOutlierDetector(resource_name: str,
                                   args: MachineLearningOutlierDetectorArgs,
                                   opts: Optional[ResourceOptions] = None)

@overload
def MachineLearningOutlierDetector(resource_name: str,
                                   opts: Optional[ResourceOptions] = None,
                                   algorithm: Optional[MachineLearningOutlierDetectorAlgorithmArgs] = None,
                                   datasource_type: Optional[str] = None,
                                   datasource_uid: Optional[str] = None,
                                   description: Optional[str] = None,
                                   interval: Optional[int] = None,
                                   metric: Optional[str] = None,
                                   name: Optional[str] = None,
                                   query_params: Optional[Mapping[str, str]] = None)
    func NewMachineLearningOutlierDetector(ctx *Context, name string, args MachineLearningOutlierDetectorArgs, opts ...ResourceOption) (*MachineLearningOutlierDetector, error)
    public MachineLearningOutlierDetector(string name, MachineLearningOutlierDetectorArgs args, CustomResourceOptions? opts = null)
    public MachineLearningOutlierDetector(String name, MachineLearningOutlierDetectorArgs args)
public MachineLearningOutlierDetector(String name, MachineLearningOutlierDetectorArgs args, CustomResourceOptions options)

    type: grafana:MachineLearningOutlierDetector
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 MachineLearningOutlierDetectorArgs
    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 MachineLearningOutlierDetectorArgs
    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 MachineLearningOutlierDetectorArgs
    The arguments to resource properties.
    opts ResourceOption
    Bag of options to control resource's behavior.
    name string
    The unique name of the resource.
    args MachineLearningOutlierDetectorArgs
    The arguments to resource properties.
    opts CustomResourceOptions
    Bag of options to control resource's behavior.
    name String
    The unique name of the resource.
    args MachineLearningOutlierDetectorArgs
    The arguments to resource properties.
    options CustomResourceOptions
    Bag of options to control resource's behavior.

    MachineLearningOutlierDetector Resource Properties

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

    Inputs

    In Python, inputs that are objects can be passed either as argument classes or as dictionary literals.

    The MachineLearningOutlierDetector resource accepts the following input properties:

    Algorithm Pulumiverse.Grafana.Inputs.MachineLearningOutlierDetectorAlgorithm
    The algorithm to use and its configuration. See https://grafana.com/docs/grafana-cloud/machine-learning/outlier-detection/ for details.
    DatasourceType string
    The type of datasource being queried. Currently allowed values are prometheus, graphite, loki, postgres, and datadog.
    DatasourceUid string
    The uid of the datasource to query.
    Metric string
    The metric used to query the outlier detector results.
    QueryParams Dictionary<string, string>
    An object representing the query params to query Grafana with.
    Description string
    A description of the outlier detector.
    Interval int
    The data interval in seconds to monitor.
    Name string
    The name of the outlier detector.
    Algorithm MachineLearningOutlierDetectorAlgorithmArgs
    The algorithm to use and its configuration. See https://grafana.com/docs/grafana-cloud/machine-learning/outlier-detection/ for details.
    DatasourceType string
    The type of datasource being queried. Currently allowed values are prometheus, graphite, loki, postgres, and datadog.
    DatasourceUid string
    The uid of the datasource to query.
    Metric string
    The metric used to query the outlier detector results.
    QueryParams map[string]string
    An object representing the query params to query Grafana with.
    Description string
    A description of the outlier detector.
    Interval int
    The data interval in seconds to monitor.
    Name string
    The name of the outlier detector.
    algorithm MachineLearningOutlierDetectorAlgorithm
    The algorithm to use and its configuration. See https://grafana.com/docs/grafana-cloud/machine-learning/outlier-detection/ for details.
    datasourceType String
    The type of datasource being queried. Currently allowed values are prometheus, graphite, loki, postgres, and datadog.
    datasourceUid String
    The uid of the datasource to query.
    metric String
    The metric used to query the outlier detector results.
    queryParams Map<String,String>
    An object representing the query params to query Grafana with.
    description String
    A description of the outlier detector.
    interval Integer
    The data interval in seconds to monitor.
    name String
    The name of the outlier detector.
    algorithm MachineLearningOutlierDetectorAlgorithm
    The algorithm to use and its configuration. See https://grafana.com/docs/grafana-cloud/machine-learning/outlier-detection/ for details.
    datasourceType string
    The type of datasource being queried. Currently allowed values are prometheus, graphite, loki, postgres, and datadog.
    datasourceUid string
    The uid of the datasource to query.
    metric string
    The metric used to query the outlier detector results.
    queryParams {[key: string]: string}
    An object representing the query params to query Grafana with.
    description string
    A description of the outlier detector.
    interval number
    The data interval in seconds to monitor.
    name string
    The name of the outlier detector.
    algorithm MachineLearningOutlierDetectorAlgorithmArgs
    The algorithm to use and its configuration. See https://grafana.com/docs/grafana-cloud/machine-learning/outlier-detection/ for details.
    datasource_type str
    The type of datasource being queried. Currently allowed values are prometheus, graphite, loki, postgres, and datadog.
    datasource_uid str
    The uid of the datasource to query.
    metric str
    The metric used to query the outlier detector results.
    query_params Mapping[str, str]
    An object representing the query params to query Grafana with.
    description str
    A description of the outlier detector.
    interval int
    The data interval in seconds to monitor.
    name str
    The name of the outlier detector.
    algorithm Property Map
    The algorithm to use and its configuration. See https://grafana.com/docs/grafana-cloud/machine-learning/outlier-detection/ for details.
    datasourceType String
    The type of datasource being queried. Currently allowed values are prometheus, graphite, loki, postgres, and datadog.
    datasourceUid String
    The uid of the datasource to query.
    metric String
    The metric used to query the outlier detector results.
    queryParams Map<String>
    An object representing the query params to query Grafana with.
    description String
    A description of the outlier detector.
    interval Number
    The data interval in seconds to monitor.
    name String
    The name of the outlier detector.

    Outputs

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

    Id string
    The provider-assigned unique ID for this managed resource.
    Id string
    The provider-assigned unique ID for this managed resource.
    id String
    The provider-assigned unique ID for this managed resource.
    id string
    The provider-assigned unique ID for this managed resource.
    id str
    The provider-assigned unique ID for this managed resource.
    id String
    The provider-assigned unique ID for this managed resource.

    Look up Existing MachineLearningOutlierDetector Resource

    Get an existing MachineLearningOutlierDetector 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?: MachineLearningOutlierDetectorState, opts?: CustomResourceOptions): MachineLearningOutlierDetector
    @staticmethod
def get(resource_name: str,
        id: str,
        opts: Optional[ResourceOptions] = None,
        algorithm: Optional[MachineLearningOutlierDetectorAlgorithmArgs] = None,
        datasource_type: Optional[str] = None,
        datasource_uid: Optional[str] = None,
        description: Optional[str] = None,
        interval: Optional[int] = None,
        metric: Optional[str] = None,
        name: Optional[str] = None,
        query_params: Optional[Mapping[str, str]] = None) -> MachineLearningOutlierDetector
    func GetMachineLearningOutlierDetector(ctx *Context, name string, id IDInput, state *MachineLearningOutlierDetectorState, opts ...ResourceOption) (*MachineLearningOutlierDetector, error)
    public static MachineLearningOutlierDetector Get(string name, Input<string> id, MachineLearningOutlierDetectorState? state, CustomResourceOptions? opts = null)
    public static MachineLearningOutlierDetector get(String name, Output<String> id, MachineLearningOutlierDetectorState state, CustomResourceOptions options)
    resources:  _:    type: grafana:MachineLearningOutlierDetector    get:      id: ${id}
    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:
    Algorithm Pulumiverse.Grafana.Inputs.MachineLearningOutlierDetectorAlgorithm
    The algorithm to use and its configuration. See https://grafana.com/docs/grafana-cloud/machine-learning/outlier-detection/ for details.
    DatasourceType string
    The type of datasource being queried. Currently allowed values are prometheus, graphite, loki, postgres, and datadog.
    DatasourceUid string
    The uid of the datasource to query.
    Description string
    A description of the outlier detector.
    Interval int
    The data interval in seconds to monitor.
    Metric string
    The metric used to query the outlier detector results.
    Name string
    The name of the outlier detector.
    QueryParams Dictionary<string, string>
    An object representing the query params to query Grafana with.
    Algorithm MachineLearningOutlierDetectorAlgorithmArgs
    The algorithm to use and its configuration. See https://grafana.com/docs/grafana-cloud/machine-learning/outlier-detection/ for details.
    DatasourceType string
    The type of datasource being queried. Currently allowed values are prometheus, graphite, loki, postgres, and datadog.
    DatasourceUid string
    The uid of the datasource to query.
    Description string
    A description of the outlier detector.
    Interval int
    The data interval in seconds to monitor.
    Metric string
    The metric used to query the outlier detector results.
    Name string
    The name of the outlier detector.
    QueryParams map[string]string
    An object representing the query params to query Grafana with.
    algorithm MachineLearningOutlierDetectorAlgorithm
    The algorithm to use and its configuration. See https://grafana.com/docs/grafana-cloud/machine-learning/outlier-detection/ for details.
    datasourceType String
    The type of datasource being queried. Currently allowed values are prometheus, graphite, loki, postgres, and datadog.
    datasourceUid String
    The uid of the datasource to query.
    description String
    A description of the outlier detector.
    interval Integer
    The data interval in seconds to monitor.
    metric String
    The metric used to query the outlier detector results.
    name String
    The name of the outlier detector.
    queryParams Map<String,String>
    An object representing the query params to query Grafana with.
    algorithm MachineLearningOutlierDetectorAlgorithm
    The algorithm to use and its configuration. See https://grafana.com/docs/grafana-cloud/machine-learning/outlier-detection/ for details.
    datasourceType string
    The type of datasource being queried. Currently allowed values are prometheus, graphite, loki, postgres, and datadog.
    datasourceUid string
    The uid of the datasource to query.
    description string
    A description of the outlier detector.
    interval number
    The data interval in seconds to monitor.
    metric string
    The metric used to query the outlier detector results.
    name string
    The name of the outlier detector.
    queryParams {[key: string]: string}
    An object representing the query params to query Grafana with.
    algorithm MachineLearningOutlierDetectorAlgorithmArgs
    The algorithm to use and its configuration. See https://grafana.com/docs/grafana-cloud/machine-learning/outlier-detection/ for details.
    datasource_type str
    The type of datasource being queried. Currently allowed values are prometheus, graphite, loki, postgres, and datadog.
    datasource_uid str
    The uid of the datasource to query.
    description str
    A description of the outlier detector.
    interval int
    The data interval in seconds to monitor.
    metric str
    The metric used to query the outlier detector results.
    name str
    The name of the outlier detector.
    query_params Mapping[str, str]
    An object representing the query params to query Grafana with.
    algorithm Property Map
    The algorithm to use and its configuration. See https://grafana.com/docs/grafana-cloud/machine-learning/outlier-detection/ for details.
    datasourceType String
    The type of datasource being queried. Currently allowed values are prometheus, graphite, loki, postgres, and datadog.
    datasourceUid String
    The uid of the datasource to query.
    description String
    A description of the outlier detector.
    interval Number
    The data interval in seconds to monitor.
    metric String
    The metric used to query the outlier detector results.
    name String
    The name of the outlier detector.
    queryParams Map<String>
    An object representing the query params to query Grafana with.

    Supporting Types

    MachineLearningOutlierDetectorAlgorithm, MachineLearningOutlierDetectorAlgorithmArgs

    Name string
    The name of the algorithm to use ('mad' or 'dbscan').
    Sensitivity double
    Specify the sensitivity of the detector (in range [0,1]).
    Config Pulumiverse.Grafana.Inputs.MachineLearningOutlierDetectorAlgorithmConfig
    For DBSCAN only, specify the configuration map
    Name string
    The name of the algorithm to use ('mad' or 'dbscan').
    Sensitivity float64
    Specify the sensitivity of the detector (in range [0,1]).
    Config MachineLearningOutlierDetectorAlgorithmConfig
    For DBSCAN only, specify the configuration map
    name String
    The name of the algorithm to use ('mad' or 'dbscan').
    sensitivity Double
    Specify the sensitivity of the detector (in range [0,1]).
    config MachineLearningOutlierDetectorAlgorithmConfig
    For DBSCAN only, specify the configuration map
    name string
    The name of the algorithm to use ('mad' or 'dbscan').
    sensitivity number
    Specify the sensitivity of the detector (in range [0,1]).
    config MachineLearningOutlierDetectorAlgorithmConfig
    For DBSCAN only, specify the configuration map
    name str
    The name of the algorithm to use ('mad' or 'dbscan').
    sensitivity float
    Specify the sensitivity of the detector (in range [0,1]).
    config MachineLearningOutlierDetectorAlgorithmConfig
    For DBSCAN only, specify the configuration map
    name String
    The name of the algorithm to use ('mad' or 'dbscan').
    sensitivity Number
    Specify the sensitivity of the detector (in range [0,1]).
    config Property Map
    For DBSCAN only, specify the configuration map

    MachineLearningOutlierDetectorAlgorithmConfig, MachineLearningOutlierDetectorAlgorithmConfigArgs

    Epsilon double
    Specify the epsilon parameter (positive float)
    Epsilon float64
    Specify the epsilon parameter (positive float)
    epsilon Double
    Specify the epsilon parameter (positive float)
    epsilon number
    Specify the epsilon parameter (positive float)
    epsilon float
    Specify the epsilon parameter (positive float)
    epsilon Number
    Specify the epsilon parameter (positive float)

    Import

    $ pulumi import grafana:index/machineLearningOutlierDetector:MachineLearningOutlierDetector name "{{ id }}"

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

    Package Details

    Repository
    grafana pulumiverse/pulumi-grafana
    License
    Apache-2.0
    Notes
    This Pulumi package is based on the grafana Terraform Provider.
    grafana logo
    Grafana v0.16.1 published on Saturday, Mar 15, 2025 by pulumiverse
    pulumiverse/pulumi-grafana