Module Values.CreateInferenceSchedulerResponseSource

Creates a scheduled inference. Scheduling an inference is setting up a continuous real-time inference plan to analyze new measurement data. When setting up the schedule, you provide an S3 bucket location for the input data, assign it a delimiter between separate entries in the data, set an offset delay if desired, and set the frequency of inferencing. You must also provide an S3 bucket location for the output data.

Sourcetype nonrec t = {
  1. inferenceSchedulerArn : InferenceSchedulerArn.t option;
    (*

    The Amazon Resource Name (ARN) of the inference scheduler being created.

    *)
  2. inferenceSchedulerName : InferenceSchedulerName.t option;
    (*

    The name of inference scheduler being created.

    *)
  3. status : InferenceSchedulerStatus.t option;
    (*

    Indicates the status of the CreateInferenceScheduler operation.

    *)
  4. modelQuality : ModelQuality.t option;
    (*

    Provides a quality assessment for a model that uses labels. If Lookout for Equipment determines that the model quality is poor based on training metrics, the value is POOR_QUALITY_DETECTED. Otherwise, the value is QUALITY_THRESHOLD_MET. If the model is unlabeled, the model quality can't be assessed and the value of ModelQuality is CANNOT_DETERMINE_QUALITY. In this situation, you can get a model quality assessment by adding labels to the input dataset and retraining the model. For information about using labels with your models, see Understanding labeling. For information about improving the quality of a model, see Best practices with Amazon Lookout for Equipment.

    *)
}
Sourcetype nonrec error = [
  1. | `AccessDeniedException of AccessDeniedException.t
  2. | `ConflictException of ConflictException.t
  3. | `InternalServerException of InternalServerException.t
  4. | `ResourceNotFoundException of ResourceNotFoundException.t
  5. | `ServiceQuotaExceededException of ServiceQuotaExceededException.t
  6. | `ThrottlingException of ThrottlingException.t
  7. | `ValidationException of ValidationException.t
  8. | `Unknown_operation_error of string * string option
]
Sourceval make : ?inferenceSchedulerArn:??? -> ?inferenceSchedulerName:??? -> ?status:??? -> ?modelQuality:??? -> unit -> t
Sourceval error_of_json : string -> Yojson.Safe.t -> [> `AccessDeniedException of AccessDeniedException.t | `ConflictException of ConflictException.t | `InternalServerException of InternalServerException.t | `ResourceNotFoundException of ResourceNotFoundException.t | `ServiceQuotaExceededException of ServiceQuotaExceededException.t | `ThrottlingException of ThrottlingException.t | `Unknown_operation_error of string * string option | `ValidationException of ValidationException.t ]
Sourceval error_of_xml : string -> Awso.Xml.t -> [> `AccessDeniedException of AccessDeniedException.t | `ConflictException of ConflictException.t | `InternalServerException of InternalServerException.t | `ResourceNotFoundException of ResourceNotFoundException.t | `ServiceQuotaExceededException of ServiceQuotaExceededException.t | `ThrottlingException of ThrottlingException.t | `Unknown_operation_error of string * string option | `ValidationException of ValidationException.t ]
Sourceval error_to_json : error -> Yojson.Safe.t
Sourceval to_value : t -> [> `Structure of (string * [> `Enum of string | `String of InferenceSchedulerArn.t ]) list ]
Sourceval to_query : t -> Awso.Client.Query.t
Sourceval of_xml : Awso.Xml.t -> t
Sourceval of_string : string -> t
Sourceval of_json : Yojson.Safe.t -> t
Sourceval to_json : t -> Yojson.Safe.t