Struct DescribeAutoMlJobOutput 

#[non_exhaustive]
pub struct DescribeAutoMlJobOutput {

    pub auto_ml_job_name: Option<String>,
    pub auto_ml_job_arn: Option<String>,
    pub input_data_config: Option<Vec<AutoMlChannel>>,
    pub output_data_config: Option<AutoMlOutputDataConfig>,
    pub role_arn: Option<String>,
    pub auto_ml_job_objective: Option<AutoMlJobObjective>,
    pub problem_type: Option<ProblemType>,
    pub auto_ml_job_config: Option<AutoMlJobConfig>,
    pub creation_time: Option<DateTime>,
    pub end_time: Option<DateTime>,
    pub last_modified_time: Option<DateTime>,
    pub failure_reason: Option<String>,
    pub partial_failure_reasons: Option<Vec<AutoMlPartialFailureReason>>,
    pub best_candidate: Option<AutoMlCandidate>,
    pub auto_ml_job_status: Option<AutoMlJobStatus>,
    pub auto_ml_job_secondary_status: Option<AutoMlJobSecondaryStatus>,
    pub generate_candidate_definitions_only: Option<bool>,
    pub auto_ml_job_artifacts: Option<AutoMlJobArtifacts>,
    pub resolved_attributes: Option<ResolvedAttributes>,
    pub model_deploy_config: Option<ModelDeployConfig>,
    pub model_deploy_result: Option<ModelDeployResult>,
    /* private fields */
}

Fields (Non-exhaustive)

Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.

auto_ml_job_name: Option<String>

Returns the name of the AutoML job.

auto_ml_job_arn: Option<String>

Returns the ARN of the AutoML job.

input_data_config: Option<Vec<AutoMlChannel>>

Returns the input data configuration for the AutoML job.

output_data_config: Option<AutoMlOutputDataConfig>

Returns the job's output data config.

role_arn: Option<String>

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.

auto_ml_job_objective: Option<AutoMlJobObjective>

Returns the job's objective.

problem_type: Option<ProblemType>

Returns the job's problem type.

auto_ml_job_config: Option<AutoMlJobConfig>

Returns the configuration for the AutoML job.

creation_time: Option<DateTime>

Returns the creation time of the AutoML job.

end_time: Option<DateTime>

Returns the end time of the AutoML job.

last_modified_time: Option<DateTime>

Returns the job's last modified time.

failure_reason: Option<String>

Returns the failure reason for an AutoML job, when applicable.

partial_failure_reasons: Option<Vec<AutoMlPartialFailureReason>>

Returns a list of reasons for partial failures within an AutoML job.

best_candidate: Option<AutoMlCandidate>

The best model candidate selected by SageMaker AI Autopilot using both the best objective metric and lowest InferenceLatency for an experiment.

auto_ml_job_status: Option<AutoMlJobStatus>

Returns the status of the AutoML job.

auto_ml_job_secondary_status: Option<AutoMlJobSecondaryStatus>

Returns the secondary status of the AutoML job.

generate_candidate_definitions_only: Option<bool>

Indicates whether the output for an AutoML job generates candidate definitions only.

auto_ml_job_artifacts: Option<AutoMlJobArtifacts>

Returns information on the job's artifacts found in AutoMLJobArtifacts.

resolved_attributes: Option<ResolvedAttributes>

Contains ProblemType, AutoMLJobObjective, and CompletionCriteria. If you do not provide these values, they are inferred.

model_deploy_config: Option<ModelDeployConfig>

Indicates whether the model was deployed automatically to an endpoint and the name of that endpoint if deployed automatically.

model_deploy_result: Option<ModelDeployResult>

Provides information about endpoint for the model deployment.

Implementations

impl DescribeAutoMlJobOutput

pub fn auto_ml_job_name(&self) -> Option<&str>

Returns the name of the AutoML job.

pub fn auto_ml_job_arn(&self) -> Option<&str>

Returns the ARN of the AutoML job.

pub fn input_data_config(&self) -> &[AutoMlChannel]

Returns the input data configuration for the AutoML job.

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .input_data_config.is_none().

pub fn output_data_config(&self) -> Option<&AutoMlOutputDataConfig>

Returns the job's output data config.

pub fn role_arn(&self) -> Option<&str>

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.

pub fn auto_ml_job_objective(&self) -> Option<&AutoMlJobObjective>

Returns the job's objective.

pub fn problem_type(&self) -> Option<&ProblemType>

Returns the job's problem type.

pub fn auto_ml_job_config(&self) -> Option<&AutoMlJobConfig>

Returns the configuration for the AutoML job.

pub fn creation_time(&self) -> Option<&DateTime>

Returns the creation time of the AutoML job.

pub fn end_time(&self) -> Option<&DateTime>

Returns the end time of the AutoML job.

pub fn last_modified_time(&self) -> Option<&DateTime>

Returns the job's last modified time.

pub fn failure_reason(&self) -> Option<&str>

Returns the failure reason for an AutoML job, when applicable.

pub fn partial_failure_reasons(&self) -> &[AutoMlPartialFailureReason]

Returns a list of reasons for partial failures within an AutoML job.

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .partial_failure_reasons.is_none().

pub fn best_candidate(&self) -> Option<&AutoMlCandidate>

The best model candidate selected by SageMaker AI Autopilot using both the best objective metric and lowest InferenceLatency for an experiment.

pub fn auto_ml_job_status(&self) -> Option<&AutoMlJobStatus>

Returns the status of the AutoML job.

pub fn auto_ml_job_secondary_status(&self) -> Option<&AutoMlJobSecondaryStatus>

Returns the secondary status of the AutoML job.

pub fn generate_candidate_definitions_only(&self) -> Option<bool>

Indicates whether the output for an AutoML job generates candidate definitions only.

pub fn auto_ml_job_artifacts(&self) -> Option<&AutoMlJobArtifacts>

Returns information on the job's artifacts found in AutoMLJobArtifacts.

pub fn resolved_attributes(&self) -> Option<&ResolvedAttributes>

Contains ProblemType, AutoMLJobObjective, and CompletionCriteria. If you do not provide these values, they are inferred.

pub fn model_deploy_config(&self) -> Option<&ModelDeployConfig>

Indicates whether the model was deployed automatically to an endpoint and the name of that endpoint if deployed automatically.

pub fn model_deploy_result(&self) -> Option<&ModelDeployResult>

Provides information about endpoint for the model deployment.

impl DescribeAutoMlJobOutput

pub fn builder() -> DescribeAutoMlJobOutputBuilder

Creates a new builder-style object to manufacture DescribeAutoMlJobOutput.

Trait Implementations

impl Clone for DescribeAutoMlJobOutput

fn clone(&self) -> DescribeAutoMlJobOutput

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for DescribeAutoMlJobOutput

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl PartialEq for DescribeAutoMlJobOutput

fn eq(&self, other: &DescribeAutoMlJobOutput) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl RequestId for DescribeAutoMlJobOutput

fn request_id(&self) -> Option<&str>

Returns the request ID, or None if the service could not be reached.

impl StructuralPartialEq for DescribeAutoMlJobOutput