Request Syntax Request Parameters Response Syntax Response Elements Errors See Also

CreateAutoMLJob

Creates an Autopilot job also referred to as Autopilot experiment or AutoML job.

An AutoML job in SageMaker is a fully automated process that allows you to build machine learning models with minimal effort and machine learning expertise. When initiating an AutoML job, you provide your data and optionally specify parameters tailored to your use case. SageMaker then automates the entire model development lifecycle, including data preprocessing, model training, tuning, and evaluation. AutoML jobs are designed to simplify and accelerate the model building process by automating various tasks and exploring different combinations of machine learning algorithms, data preprocessing techniques, and hyperparameter values. The output of an AutoML job comprises one or more trained models ready for deployment and inference. Additionally, SageMaker AutoML jobs generate a candidate model leaderboard, allowing you to select the best-performing model for deployment.

For more information about AutoML jobs, see https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development.html in the SageMaker developer guide.

Note

We recommend using the new versions CreateAutoMLJobV2 and DescribeAutoMLJobV2, which offer backward compatibility.

CreateAutoMLJobV2 can manage tabular problem types identical to those of its previous version CreateAutoMLJob, as well as time-series forecasting, non-tabular problem types such as image or text classification, and text generation (LLMs fine-tuning).

Find guidelines about how to migrate a CreateAutoMLJob to CreateAutoMLJobV2 in Migrate a CreateAutoMLJob to CreateAutoMLJobV2.

You can find the best-performing model after you run an AutoML job by calling DescribeAutoMLJobV2 (recommended) or DescribeAutoMLJob.

Request Syntax


{
   "AutoMLJobConfig": { 
      "CandidateGenerationConfig": { 
         "AlgorithmsConfig": [ 
            { 
               "AutoMLAlgorithms": [ "string" ]
            }
         ],
         "FeatureSpecificationS3Uri": "string"
      },
      "CompletionCriteria": { 
         "MaxAutoMLJobRuntimeInSeconds": number,
         "MaxCandidates": number,
         "MaxRuntimePerTrainingJobInSeconds": number
      },
      "DataSplitConfig": { 
         "ValidationFraction": number
      },
      "Mode": "string",
      "SecurityConfig": { 
         "EnableInterContainerTrafficEncryption": boolean,
         "VolumeKmsKeyId": "string",
         "VpcConfig": { 
            "SecurityGroupIds": [ "string" ],
            "Subnets": [ "string" ]
         }
      }
   },
   "AutoMLJobName": "string",
   "AutoMLJobObjective": { 
      "MetricName": "string"
   },
   "GenerateCandidateDefinitionsOnly": boolean,
   "InputDataConfig": [ 
      { 
         "ChannelType": "string",
         "CompressionType": "string",
         "ContentType": "string",
         "DataSource": { 
            "S3DataSource": { 
               "S3DataType": "string",
               "S3Uri": "string"
            }
         },
         "SampleWeightAttributeName": "string",
         "TargetAttributeName": "string"
      }
   ],
   "ModelDeployConfig": { 
      "AutoGenerateEndpointName": boolean,
      "EndpointName": "string"
   },
   "OutputDataConfig": { 
      "KmsKeyId": "string",
      "S3OutputPath": "string"
   },
   "ProblemType": "string",
   "RoleArn": "string",
   "Tags": [ 
      { 
         "Key": "string",
         "Value": "string"
      }
   ]
}

Request Parameters

For information about the parameters that are common to all actions, see Common Parameters.

The request accepts the following data in JSON format.

AutoMLJobConfig

A collection of settings used to configure an AutoML job.

Type: AutoMLJobConfig object

Required: No

AutoMLJobName

Identifies an Autopilot job. The name must be unique to your account and is case insensitive.

Type: String

Length Constraints: Minimum length of 1. Maximum length of 32.

Pattern: ^[a-zA-Z0-9](-*[a-zA-Z0-9]){0,31}

Required: Yes

AutoMLJobObjective

Specifies a metric to minimize or maximize as the objective of a job. If not specified, the default objective metric depends on the problem type. See AutoMLJobObjective for the default values.

Type: AutoMLJobObjective object

Required: No

GenerateCandidateDefinitionsOnly

Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

Type: Boolean

Required: No

InputDataConfig

An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to InputDataConfig supported by HyperParameterTrainingJobDefinition. Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.

Type: Array of AutoMLChannel objects

Array Members: Minimum number of 1 item. Maximum number of 2 items.

Required: Yes

ModelDeployConfig

Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.

Type: ModelDeployConfig object

Required: No

OutputDataConfig

Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job. Format(s) supported: CSV.

Type: AutoMLOutputDataConfig object

Required: Yes

ProblemType

Defines the type of supervised learning problem available for the candidates. For more information, see SageMaker Autopilot problem types.

Type: String

Valid Values: BinaryClassification | MulticlassClassification | Regression

Required: No

RoleArn

The ARN of the role that is used to access the data.

Type: String

Length Constraints: Minimum length of 20. Maximum length of 2048.

Pattern: ^arn:aws[a-z\-]*:iam::\d{12}:role/?[a-zA-Z_0-9+=,.@\-_/]+$

Required: Yes

Tags

An array of key-value pairs. You can use tags to categorize your AWS resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging AWSResources. Tag keys must be unique per resource.

Type: Array of Tag objects

Array Members: Minimum number of 0 items. Maximum number of 50 items.

Required: No

Response Syntax


{
   "AutoMLJobArn": "string"
}

Response Elements

If the action is successful, the service sends back an HTTP 200 response.

The following data is returned in JSON format by the service.

AutoMLJobArn

The unique ARN assigned to the AutoML job when it is created.

Type: String

Length Constraints: Minimum length of 1. Maximum length of 256.

Pattern: arn:aws[a-z\-]*:sagemaker:[a-z0-9\-]*:[0-9]{12}:automl-job/.*

Errors

For information about the errors that are common to all actions, see Common Errors.

ResourceInUse

Resource being accessed is in use.

HTTP Status Code: 400

ResourceLimitExceeded

You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

HTTP Status Code: 400