pub struct CreateAutoMLJobRequest {
pub auto_ml_job_config: Option<AutoMLJobConfig>,
pub auto_ml_job_name: String,
pub auto_ml_job_objective: Option<AutoMLJobObjective>,
pub generate_candidate_definitions_only: Option<bool>,
pub input_data_config: Vec<AutoMLChannel>,
pub output_data_config: AutoMLOutputDataConfig,
pub problem_type: Option<String>,
pub role_arn: String,
pub tags: Option<Vec<Tag>>,
}
Fields§
§auto_ml_job_config: Option<AutoMLJobConfig>
Contains CompletionCriteria and SecurityConfig.
auto_ml_job_name: String
Identifies an AutoPilot job. Must be unique to your account and is case-insensitive.
auto_ml_job_objective: Option<AutoMLJobObjective>
Defines the job's objective. You provide a MetricName and AutoML will infer minimize or maximize. If this is not provided, the most commonly used ObjectiveMetric for problem type will be selected.
generate_candidate_definitions_only: Option<bool>
This will generate possible candidates without training a model. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.
input_data_config: Vec<AutoMLChannel>
Similar to InputDataConfig supported by Tuning. Format(s) supported: CSV. Minimum of 1000 rows.
output_data_config: AutoMLOutputDataConfig
Similar to OutputDataConfig supported by Tuning. Format(s) supported: CSV.
problem_type: Option<String>
Defines the kind of preprocessing and algorithms intended for the candidates. Options include: BinaryClassification, MulticlassClassification, and Regression.
role_arn: String
The ARN of the role that will be used to access the data.
Each tag consists of a key and an optional value. Tag keys must be unique per resource.
Trait Implementations§
Source§impl Clone for CreateAutoMLJobRequest
impl Clone for CreateAutoMLJobRequest
Source§fn clone(&self) -> CreateAutoMLJobRequest
fn clone(&self) -> CreateAutoMLJobRequest
1.0.0 · Source§fn clone_from(&mut self, source: &Self)
fn clone_from(&mut self, source: &Self)
source
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