---
title: Getting Started with Datadog
description: Datadog, the leading service for cloud-scale monitoring.
breadcrumbs: Docs > Infrastructure > Datadog Resource Catalog
---

# aws_sagemaker_automljob{% #aws_sagemaker_automljob %}

## `account_id`{% #account_id %}

**Type**: `STRING`

## `auto_ml_job_arn`{% #auto_ml_job_arn %}

**Type**: `STRING`**Provider name**: `AutoMLJobArn`**Description**: Returns the ARN of the AutoML job.

## `auto_ml_job_artifacts`{% #auto_ml_job_artifacts %}

**Type**: `STRUCT`**Provider name**: `AutoMLJobArtifacts`**Description**: Returns information on the job's artifacts found in `AutoMLJobArtifacts`.

- `candidate_definition_notebook_location`**Type**: `STRING`**Provider name**: `CandidateDefinitionNotebookLocation`**Description**: The URL of the notebook location.
- `data_exploration_notebook_location`**Type**: `STRING`**Provider name**: `DataExplorationNotebookLocation`**Description**: The URL of the notebook location.

## `auto_ml_job_config`{% #auto_ml_job_config %}

**Type**: `STRUCT`**Provider name**: `AutoMLJobConfig`**Description**: Returns the configuration for the AutoML job.

- `candidate_generation_config`**Type**: `STRUCT`**Provider name**: `CandidateGenerationConfig`**Description**: The configuration for generating a candidate for an AutoML job (optional).
  - `algorithms_config`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `AlgorithmsConfig`**Description**: Stores the configuration information for the selection of algorithms trained on tabular data. The list of available algorithms to choose from depends on the training mode set in [`TabularJobConfig.Mode` ](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_TabularJobConfig.html).
    - `AlgorithmsConfig` should not be set if the training mode is set on `AUTO`.
    - When `AlgorithmsConfig` is provided, one `AutoMLAlgorithms` attribute must be set and one only. If the list of algorithms provided as values for `AutoMLAlgorithms` is empty, `CandidateGenerationConfig` uses the full set of algorithms for the given training mode.
    - When `AlgorithmsConfig` is not provided, `CandidateGenerationConfig` uses the full set of algorithms for the given training mode.
For the list of all algorithms per problem type and training mode, see [AutoMLAlgorithmConfig](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AutoMLAlgorithmConfig.html). For more information on each algorithm, see the [Algorithm support](https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-model-support-validation.html#autopilot-algorithm-support) section in Autopilot developer guide.
    - `auto_ml_algorithms`**Type**: `UNORDERED_LIST_STRING`**Provider name**: `AutoMLAlgorithms`**Description**: The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.
      - For the tabular problem type `TabularJobConfig`:Selected algorithms must belong to the list corresponding to the training mode set in [AutoMLJobConfig.Mode](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AutoMLJobConfig.html#sagemaker-Type-AutoMLJobConfig-Mode) (`ENSEMBLING` or `HYPERPARAMETER_TUNING`). Choose a minimum of 1 algorithm.
        - In `ENSEMBLING` mode:
          - "catboost"
          - "extra-trees"
          - "fastai"
          - "lightgbm"
          - "linear-learner"
          - "nn-torch"
          - "randomforest"
          - "xgboost"
        - In `HYPERPARAMETER_TUNING` mode:
          - "linear-learner"
          - "mlp"
          - "xgboost"
      - For the time-series forecasting problem type `TimeSeriesForecastingJobConfig`:
        - Choose your algorithms from this list.
          - "cnn-qr"
          - "deepar"
          - "prophet"
          - "arima"
          - "npts"
          - "ets"
  - `feature_specification_s3_uri`**Type**: `STRING`**Provider name**: `FeatureSpecificationS3Uri`**Description**: A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot job. You can input `FeatureAttributeNames` (optional) in JSON format as shown below: `{ "FeatureAttributeNames":["col1", "col2", …] }`. You can also specify the data type of the feature (optional) in the format shown below: `{ "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" … } }`These column keys may not include the target column.In ensembling mode, Autopilot only supports the following data types: `numeric`, `categorical`, `text`, and `datetime`. In HPO mode, Autopilot can support `numeric`, `categorical`, `text`, `datetime`, and `sequence`. If only `FeatureDataTypes` is provided, the column keys (`col1`, `col2`,..) should be a subset of the column names in the input data. If both `FeatureDataTypes` and `FeatureAttributeNames` are provided, then the column keys should be a subset of the column names provided in `FeatureAttributeNames`. The key name `FeatureAttributeNames` is fixed. The values listed in `["col1", "col2", …]` are case sensitive and should be a list of strings containing unique values that are a subset of the column names in the input data. The list of columns provided must not include the target column.
- `completion_criteria`**Type**: `STRUCT`**Provider name**: `CompletionCriteria`**Description**: How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.
  - `max_auto_ml_job_runtime_in_seconds`**Type**: `INT32`**Provider name**: `MaxAutoMLJobRuntimeInSeconds`**Description**: The maximum runtime, in seconds, an AutoML job has to complete. If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.
  - `max_candidates`**Type**: `INT32`**Provider name**: `MaxCandidates`**Description**: The maximum number of times a training job is allowed to run. For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.
  - `max_runtime_per_training_job_in_seconds`**Type**: `INT32`**Provider name**: `MaxRuntimePerTrainingJobInSeconds`**Description**: The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the [StoppingCondition](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_StoppingCondition.html) used by the [CreateHyperParameterTuningJob](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateHyperParameterTuningJob.html) action. For job V2s (jobs created by calling `CreateAutoMLJobV2`), this field controls the runtime of the job candidate. For [TextGenerationJobConfig](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_TextClassificationJobConfig.html) problem types, the maximum time defaults to 72 hours (259200 seconds).
- `data_split_config`**Type**: `STRUCT`**Provider name**: `DataSplitConfig`**Description**: The configuration for splitting the input training dataset. Type: AutoMLDataSplitConfig
  - `validation_fraction`**Type**: `FLOAT`**Provider name**: `ValidationFraction`**Description**: The validation fraction (optional) is a float that specifies the portion of the training dataset to be used for validation. The default value is 0.2, and values must be greater than 0 and less than 1. We recommend setting this value to be less than 0.5.
- `mode`**Type**: `STRING`**Provider name**: `Mode`**Description**: The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting `AUTO`. In `AUTO` mode, Autopilot chooses `ENSEMBLING` for datasets smaller than 100 MB, and `HYPERPARAMETER_TUNING` for larger ones. The `ENSEMBLING` mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See [Autopilot algorithm support](https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-model-support-validation.html#autopilot-algorithm-support) for a list of algorithms supported by `ENSEMBLING` mode. The `HYPERPARAMETER_TUNING` (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See [Autopilot algorithm support](https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-model-support-validation.html#autopilot-algorithm-support) for a list of algorithms supported by `HYPERPARAMETER_TUNING` mode.
- `security_config`**Type**: `STRUCT`**Provider name**: `SecurityConfig`**Description**: The security configuration for traffic encryption or Amazon VPC settings.
  - `enable_inter_container_traffic_encryption`**Type**: `BOOLEAN`**Provider name**: `EnableInterContainerTrafficEncryption`**Description**: Whether to use traffic encryption between the container layers.
  - `volume_kms_key_id`**Type**: `STRING`**Provider name**: `VolumeKmsKeyId`**Description**: The key used to encrypt stored data.
  - `vpc_config`**Type**: `STRUCT`**Provider name**: `VpcConfig`**Description**: The VPC configuration.
    - `security_group_ids`**Type**: `UNORDERED_LIST_STRING`**Provider name**: `SecurityGroupIds`**Description**: The VPC security group IDs, in the form `sg-xxxxxxxx`. Specify the security groups for the VPC that is specified in the `Subnets` field.
    - `subnets`**Type**: `UNORDERED_LIST_STRING`**Provider name**: `Subnets`**Description**: The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see [Supported Instance Types and Availability Zones](https://docs.aws.amazon.com/sagemaker/latest/dg/instance-types-az.html).

## `auto_ml_job_name`{% #auto_ml_job_name %}

**Type**: `STRING`**Provider name**: `AutoMLJobName`**Description**: Returns the name of the AutoML job.

## `auto_ml_job_objective`{% #auto_ml_job_objective %}

**Type**: `STRUCT`**Provider name**: `AutoMLJobObjective`**Description**: Returns the job's objective.

- `metric_name`**Type**: `STRING`**Provider name**: `MetricName`**Description**: The name of the objective metric used to measure the predictive quality of a machine learning system. During training, the model's parameters are updated iteratively to optimize its performance based on the feedback provided by the objective metric when evaluating the model on the validation dataset. The list of available metrics supported by Autopilot and the default metric applied when you do not specify a metric name explicitly depend on the problem type.
  - For tabular problem types:
    - List of available metrics:
      - Regression: `MAE`, `MSE`, `R2`, `RMSE`
      - Binary classification: `Accuracy`, `AUC`, `BalancedAccuracy`, `F1`, `Precision`, `Recall`
      - Multiclass classification: `Accuracy`, `BalancedAccuracy`, `F1macro`, `PrecisionMacro`, `RecallMacro`
For a description of each metric, see [Autopilot metrics for classification and regression](https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-metrics-validation.html#autopilot-metrics).
    - Default objective metrics:
      - Regression: `MSE`.
      - Binary classification: `F1`.
      - Multiclass classification: `Accuracy`.
  - For image or text classification problem types:
    - List of available metrics: `Accuracy` For a description of each metric, see [Autopilot metrics for text and image classification](https://docs.aws.amazon.com/sagemaker/latest/dg/text-classification-data-format-and-metric.html).
    - Default objective metrics: `Accuracy`
  - For time-series forecasting problem types:
    - List of available metrics: `RMSE`, `wQL`, `Average wQL`, `MASE`, `MAPE`, `WAPE` For a description of each metric, see [Autopilot metrics for time-series forecasting](https://docs.aws.amazon.com/sagemaker/latest/dg/timeseries-objective-metric.html).
    - Default objective metrics: `AverageWeightedQuantileLoss`
  - For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the `AutoMLJobObjective` field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see [Metrics for fine-tuning LLMs in Autopilot](https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-llms-finetuning-metrics.html).

## `auto_ml_job_secondary_status`{% #auto_ml_job_secondary_status %}

**Type**: `STRING`**Provider name**: `AutoMLJobSecondaryStatus`**Description**: Returns the secondary status of the AutoML job.

## `auto_ml_job_status`{% #auto_ml_job_status %}

**Type**: `STRING`**Provider name**: `AutoMLJobStatus`**Description**: Returns the status of the AutoML job.

## `best_candidate`{% #best_candidate %}

**Type**: `STRUCT`**Provider name**: `BestCandidate`**Description**: The best model candidate selected by SageMaker AI Autopilot using both the best objective metric and lowest [InferenceLatency](https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-metrics-validation.html) for an experiment.

- `candidate_name`**Type**: `STRING`**Provider name**: `CandidateName`**Description**: The name of the candidate.
- `candidate_properties`**Type**: `STRUCT`**Provider name**: `CandidateProperties`**Description**: The properties of an AutoML candidate job.
  - `candidate_artifact_locations`**Type**: `STRUCT`**Provider name**: `CandidateArtifactLocations`**Description**: The Amazon S3 prefix to the artifacts generated for an AutoML candidate.
    - `backtest_results`**Type**: `STRING`**Provider name**: `BacktestResults`**Description**: The Amazon S3 prefix to the accuracy metrics and the inference results observed over the testing window. Available only for the time-series forecasting problem type.
    - `explainability`**Type**: `STRING`**Provider name**: `Explainability`**Description**: The Amazon S3 prefix to the explainability artifacts generated for the AutoML candidate.
    - `model_insights`**Type**: `STRING`**Provider name**: `ModelInsights`**Description**: The Amazon S3 prefix to the model insight artifacts generated for the AutoML candidate.
  - `candidate_metrics`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `CandidateMetrics`**Description**: Information about the candidate metrics for an AutoML job.
    - `metric_name`**Type**: `STRING`**Provider name**: `MetricName`**Description**: The name of the metric.
    - `set`**Type**: `STRING`**Provider name**: `Set`**Description**: The dataset split from which the AutoML job produced the metric.
    - `standard_metric_name`**Type**: `STRING`**Provider name**: `StandardMetricName`**Description**: The name of the standard metric.For definitions of the standard metrics, see [`Autopilot candidate metrics` ](https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-model-support-validation.html#autopilot-metrics).
    - `value`**Type**: `FLOAT`**Provider name**: `Value`**Description**: The value of the metric.
- `candidate_status`**Type**: `STRING`**Provider name**: `CandidateStatus`**Description**: The candidate's status.
- `candidate_steps`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `CandidateSteps`**Description**: Information about the candidate's steps.
  - `candidate_step_arn`**Type**: `STRING`**Provider name**: `CandidateStepArn`**Description**: The ARN for the candidate's step.
  - `candidate_step_name`**Type**: `STRING`**Provider name**: `CandidateStepName`**Description**: The name for the candidate's step.
  - `candidate_step_type`**Type**: `STRING`**Provider name**: `CandidateStepType`**Description**: Whether the candidate is at the transform, training, or processing step.
- `creation_time`**Type**: `TIMESTAMP`**Provider name**: `CreationTime`**Description**: The creation time.
- `end_time`**Type**: `TIMESTAMP`**Provider name**: `EndTime`**Description**: The end time.
- `failure_reason`**Type**: `STRING`**Provider name**: `FailureReason`**Description**: The failure reason.
- `final_auto_ml_job_objective_metric`**Type**: `STRUCT`**Provider name**: `FinalAutoMLJobObjectiveMetric`
  - `metric_name`**Type**: `STRING`**Provider name**: `MetricName`**Description**: The name of the metric with the best result. For a description of the possible objective metrics, see [AutoMLJobObjective$MetricName](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AutoMLJobObjective.html).
  - `standard_metric_name`**Type**: `STRING`**Provider name**: `StandardMetricName`**Description**: The name of the standard metric. For a description of the standard metrics, see [Autopilot candidate metrics](https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-metrics-validation.html#autopilot-metrics).
  - `type`**Type**: `STRING`**Provider name**: `Type`**Description**: The type of metric with the best result.
  - `value`**Type**: `FLOAT`**Provider name**: `Value`**Description**: The value of the metric with the best result.
- `inference_container_definitions`**Type**: `STRING`**Provider name**: `InferenceContainerDefinitions`**Description**: The mapping of all supported processing unit (CPU, GPU, etc…) to inference container definitions for the candidate. This field is populated for the AutoML jobs V2 (for example, for jobs created by calling `CreateAutoMLJobV2`) related to image or text classification problem types only.
- `inference_containers`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `InferenceContainers`**Description**: Information about the recommended inference container definitions.
  - `environment`**Type**: `MAP_STRING_STRING`**Provider name**: `Environment`**Description**: The environment variables to set in the container. For more information, see [ContainerDefinition](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ContainerDefinition.html).
  - `image`**Type**: `STRING`**Provider name**: `Image`**Description**: The Amazon Elastic Container Registry (Amazon ECR) path of the container. For more information, see [ContainerDefinition](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ContainerDefinition.html).
  - `model_data_url`**Type**: `STRING`**Provider name**: `ModelDataUrl`**Description**: The location of the model artifacts. For more information, see [ContainerDefinition](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ContainerDefinition.html).
- `last_modified_time`**Type**: `TIMESTAMP`**Provider name**: `LastModifiedTime`**Description**: The last modified time.
- `objective_status`**Type**: `STRING`**Provider name**: `ObjectiveStatus`**Description**: The objective's status.

## `creation_time`{% #creation_time %}

**Type**: `TIMESTAMP`**Provider name**: `CreationTime`**Description**: Returns the creation time of the AutoML job.

## `end_time`{% #end_time %}

**Type**: `TIMESTAMP`**Provider name**: `EndTime`**Description**: Returns the end time of the AutoML job.

## `failure_reason`{% #failure_reason %}

**Type**: `STRING`**Provider name**: `FailureReason`**Description**: Returns the failure reason for an AutoML job, when applicable.

## `generate_candidate_definitions_only`{% #generate_candidate_definitions_only %}

**Type**: `BOOLEAN`**Provider name**: `GenerateCandidateDefinitionsOnly`**Description**: Indicates whether the output for an AutoML job generates candidate definitions only.

## `input_data_config`{% #input_data_config %}

**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `InputDataConfig`**Description**: Returns the input data configuration for the AutoML job.

- `channel_type`**Type**: `STRING`**Provider name**: `ChannelType`**Description**: The channel type (optional) is an `enum` string. The default value is `training`. Channels for training and validation must share the same `ContentType` and `TargetAttributeName`. For information on specifying training and validation channel types, see [How to specify training and validation datasets](https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-datasets-problem-types.html#autopilot-data-sources-training-or-validation).
- `compression_type`**Type**: `STRING`**Provider name**: `CompressionType`**Description**: You can use `Gzip` or `None`. The default value is `None`.
- `content_type`**Type**: `STRING`**Provider name**: `ContentType`**Description**: The content type of the data from the input source. You can use `text/csv;header=present` or `x-application/vnd.amazon+parquet`. The default value is `text/csv;header=present`.
- `data_source`**Type**: `STRUCT`**Provider name**: `DataSource`**Description**: The data source for an AutoML channel.
  - `s3_data_source`**Type**: `STRUCT`**Provider name**: `S3DataSource`**Description**: The Amazon S3 location of the input data.
    - `s3_data_type`**Type**: `STRING`**Provider name**: `S3DataType`**Description**: The data type.
      - If you choose `S3Prefix`, `S3Uri` identifies a key name prefix. SageMaker AI uses all objects that match the specified key name prefix for model training. The `S3Prefix` should have the following format: `s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER-OR-FILE`
      - If you choose `ManifestFile`, `S3Uri` identifies an object that is a manifest file containing a list of object keys that you want SageMaker AI to use for model training. A `ManifestFile` should have the format shown below: `[ {"prefix": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/DOC-EXAMPLE-PREFIX/"},``"DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-1",` `"DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-2",` `… "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-N" ]`
      - If you choose `AugmentedManifestFile`, `S3Uri` identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. `AugmentedManifestFile` is available for V2 API jobs only (for example, for jobs created by calling `CreateAutoMLJobV2`). Here is a minimal, single-record example of an `AugmentedManifestFile`: `{"source-ref": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/cats/cat.jpg",` `"label-metadata": {"class-name": "cat"` } For more information on `AugmentedManifestFile`, see [Provide Dataset Metadata to Training Jobs with an Augmented Manifest File](https://docs.aws.amazon.com/sagemaker/latest/dg/augmented-manifest.html).
    - `s3_uri`**Type**: `STRING`**Provider name**: `S3Uri`**Description**: The URL to the Amazon S3 data source. The Uri refers to the Amazon S3 prefix or ManifestFile depending on the data type.
- `sample_weight_attribute_name`**Type**: `STRING`**Provider name**: `SampleWeightAttributeName`**Description**: If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model. This column is not considered as a predictive feature. For more information on Autopilot metrics, see [Metrics and validation](https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-metrics-validation.html). Sample weights should be numeric, non-negative, with larger values indicating which rows are more important than others. Data points that have invalid or no weight value are excluded. Support for sample weights is available in [Ensembling](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AutoMLAlgorithmConfig.html) mode only.
- `target_attribute_name`**Type**: `STRING`**Provider name**: `TargetAttributeName`**Description**: The name of the target variable in supervised learning, usually represented by 'y'.

## `last_modified_time`{% #last_modified_time %}

**Type**: `TIMESTAMP`**Provider name**: `LastModifiedTime`**Description**: Returns the job's last modified time.

## `model_deploy_config`{% #model_deploy_config %}

**Type**: `STRUCT`**Provider name**: `ModelDeployConfig`**Description**: Indicates whether the model was deployed automatically to an endpoint and the name of that endpoint if deployed automatically.

- `auto_generate_endpoint_name`**Type**: `BOOLEAN`**Provider name**: `AutoGenerateEndpointName`**Description**: Set to `True` to automatically generate an endpoint name for a one-click Autopilot model deployment; set to `False` otherwise. The default value is `False`.If you set `AutoGenerateEndpointName` to `True`, do not specify the `EndpointName`; otherwise a 400 error is thrown.
- `endpoint_name`**Type**: `STRING`**Provider name**: `EndpointName`**Description**: Specifies the endpoint name to use for a one-click Autopilot model deployment if the endpoint name is not generated automatically.Specify the `EndpointName` if and only if you set `AutoGenerateEndpointName` to `False`; otherwise a 400 error is thrown.

## `model_deploy_result`{% #model_deploy_result %}

**Type**: `STRUCT`**Provider name**: `ModelDeployResult`**Description**: Provides information about endpoint for the model deployment.

- `endpoint_name`**Type**: `STRING`**Provider name**: `EndpointName`**Description**: The name of the endpoint to which the model has been deployed.If model deployment fails, this field is omitted from the response.

## `output_data_config`{% #output_data_config %}

**Type**: `STRUCT`**Provider name**: `OutputDataConfig`**Description**: Returns the job's output data config.

- `kms_key_id`**Type**: `STRING`**Provider name**: `KmsKeyId`**Description**: The Key Management Service encryption key ID.
- `s3_output_path`**Type**: `STRING`**Provider name**: `S3OutputPath`**Description**: The Amazon S3 output path. Must be 512 characters or less.

## `partial_failure_reasons`{% #partial_failure_reasons %}

**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `PartialFailureReasons`**Description**: Returns a list of reasons for partial failures within an AutoML job.

- `partial_failure_message`**Type**: `STRING`**Provider name**: `PartialFailureMessage`**Description**: The message containing the reason for a partial failure of an AutoML job.

## `problem_type`{% #problem_type %}

**Type**: `STRING`**Provider name**: `ProblemType`**Description**: Returns the job's problem type.

## `resolved_attributes`{% #resolved_attributes %}

**Type**: `STRUCT`**Provider name**: `ResolvedAttributes`**Description**: Contains `ProblemType`, `AutoMLJobObjective`, and `CompletionCriteria`. If you do not provide these values, they are inferred.

- `auto_ml_job_objective`**Type**: `STRUCT`**Provider name**: `AutoMLJobObjective`
  - `metric_name`**Type**: `STRING`**Provider name**: `MetricName`**Description**: The name of the objective metric used to measure the predictive quality of a machine learning system. During training, the model's parameters are updated iteratively to optimize its performance based on the feedback provided by the objective metric when evaluating the model on the validation dataset. The list of available metrics supported by Autopilot and the default metric applied when you do not specify a metric name explicitly depend on the problem type.
    - For tabular problem types:
      - List of available metrics:
        - Regression: `MAE`, `MSE`, `R2`, `RMSE`
        - Binary classification: `Accuracy`, `AUC`, `BalancedAccuracy`, `F1`, `Precision`, `Recall`
        - Multiclass classification: `Accuracy`, `BalancedAccuracy`, `F1macro`, `PrecisionMacro`, `RecallMacro`
For a description of each metric, see [Autopilot metrics for classification and regression](https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-metrics-validation.html#autopilot-metrics).
      - Default objective metrics:
        - Regression: `MSE`.
        - Binary classification: `F1`.
        - Multiclass classification: `Accuracy`.
    - For image or text classification problem types:
      - List of available metrics: `Accuracy` For a description of each metric, see [Autopilot metrics for text and image classification](https://docs.aws.amazon.com/sagemaker/latest/dg/text-classification-data-format-and-metric.html).
      - Default objective metrics: `Accuracy`
    - For time-series forecasting problem types:
      - List of available metrics: `RMSE`, `wQL`, `Average wQL`, `MASE`, `MAPE`, `WAPE` For a description of each metric, see [Autopilot metrics for time-series forecasting](https://docs.aws.amazon.com/sagemaker/latest/dg/timeseries-objective-metric.html).
      - Default objective metrics: `AverageWeightedQuantileLoss`
    - For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the `AutoMLJobObjective` field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see [Metrics for fine-tuning LLMs in Autopilot](https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-llms-finetuning-metrics.html).
- `completion_criteria`**Type**: `STRUCT`**Provider name**: `CompletionCriteria`
  - `max_auto_ml_job_runtime_in_seconds`**Type**: `INT32`**Provider name**: `MaxAutoMLJobRuntimeInSeconds`**Description**: The maximum runtime, in seconds, an AutoML job has to complete. If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.
  - `max_candidates`**Type**: `INT32`**Provider name**: `MaxCandidates`**Description**: The maximum number of times a training job is allowed to run. For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.
  - `max_runtime_per_training_job_in_seconds`**Type**: `INT32`**Provider name**: `MaxRuntimePerTrainingJobInSeconds`**Description**: The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the [StoppingCondition](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_StoppingCondition.html) used by the [CreateHyperParameterTuningJob](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateHyperParameterTuningJob.html) action. For job V2s (jobs created by calling `CreateAutoMLJobV2`), this field controls the runtime of the job candidate. For [TextGenerationJobConfig](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_TextClassificationJobConfig.html) problem types, the maximum time defaults to 72 hours (259200 seconds).
- `problem_type`**Type**: `STRING`**Provider name**: `ProblemType`**Description**: The problem type.

## `role_arn`{% #role_arn %}

**Type**: `STRING`**Provider name**: `RoleArn`**Description**: The ARN of the IAM role that has read permission to the input data location and write permission to the output data location in Amazon S3.

## `tags`{% #tags %}

**Type**: `UNORDERED_LIST_STRING`