Required parameters Optional parameters Migrate a CreateAutoMLJob to CreateAutoMLJobV2

Create a regression or classification job for tabular data using the AutoML API

You can create an Autopilot experiment for tabular data programmatically by calling the CreateAutoMLJobV2 API action in any language supported by Autopilot or the AWS CLI.

For information on how this API action translates into a function in the language of your choice, see the See Also section of CreateAutoMLJobV2 and choose an SDK. As an example, for Python users, see the full request syntax of create_auto_ml_job_v2 in AWS SDK for Python (Boto3).

Note

CreateAutoMLJobV2 and DescribeAutoMLJobV2 are new versions of CreateAutoMLJob and DescribeAutoMLJob which offer backward compatibility.

We recommend using the CreateAutoMLJobV2. CreateAutoMLJobV2 can manage tabular problem types identical to those of its previous version CreateAutoMLJob, as well as non-tabular problem types such as image or text classification, or time-series forecasting.

At a minimum, all experiments on tabular data require the specification of the experiment name, providing locations for the input and output data, and specifying which target data to predict. Optionally, you can also specify the type of problem that you want to solve (regression, classification, multiclass classification), choose your modeling strategy (stacked ensembles or hyperparameters optimization), select the list of algorithms used by the Autopilot job to train the data, and more.

After the experiment runs, you can compare trials and delve into the details of the pre-processing steps, algorithms, and hyperparameter ranges of each model. You also have the option to download their explainability and performance reports. Use the provided notebooks to see the results of the automated data exploration or the candidate model definitions.

The following is a collection of mandatory and optional input request parameters for the CreateAutoMLJobV2 API action. You can find the alternative information for the previous version of this action, CreateAutoMLJob. However, we recommend using CreateAutoMLJobV2.

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

Required parameters

All other parameters are optional.

Optional parameters

The following sections provide details of some optional parameters that you can pass to your CreateAutoMLJobV2 API action when using tabular data. You can find the alternative information for the previous version of this action, CreateAutoMLJob. However, we recommend using CreateAutoMLJobV2.

For tabular data, the set of algorithms run on your data to train your model candidates is dependent on your modeling strategy (ENSEMBLING or HYPERPARAMETER_TUNING). The following details how to set this training mode.

If you keep blank (or null), the Mode is inferred based on the size of your dataset.

For information on Autopilot's stacked ensembles and hyperparameters optimization training methods, see Training modes and algorithm support

Features selection

Autopilot provides automatic data-preprocessing steps including feature selection and feature extraction. However, you can manually provide the features to be used in training with the FeatureSpecificatioS3Uri attribute.

Selected features should be contained within a JSON file in the following format:


{ "FeatureAttributeNames":["col1", "col2", ...] }

The values listed in ["col1", "col2", ...] are case sensitive. They should be a list of strings containing unique values that are subsets of the column names in the input data.

Note

The list of columns provided as features cannot include the target column.

Algorithms selection

By default, your Autopilot job runs a pre-defined list of algorithms on your dataset to train model candidates. The list of algorithms depends on the training mode (ENSEMBLING or HYPERPARAMETER_TUNING) used by the job.

You can provide a subset of the default selection of algorithms.

For the list of available algorithms per training Mode, see AutoMLAlgorithms. For details on each algorithm, see Training modes and algorithm support.

You can provide your own validation dataset and custom data split ratio, or let Autopilot split the dataset automatically.

CreateAutoMLJobV2

Each AutoMLJobChannel object (see the required parameter AutoMLJobInputDataConfig) has a ChannelType, which can be set to either training or validation values that specify how the data is to be used when building a machine learning model. At least one data source must be provided and a maximum of two data sources is allowed: one for training data and one for validation data.

How you split the data into training and validation datasets depends on whether you have one or two data sources.

If you only have one data source, the ChannelType is set to training by default and must have this value.
- If the ValidationFraction value in AutoMLDataSplitConfig is not set, 0.2 (20%) of the data from this source is used for validation by default.
- If the ValidationFraction is set to a value between 0 and 1, the dataset is split based on the value specified, where the value specifies the fraction of the dataset used for validation.
If you have two data sources, the ChannelType of one of the AutoMLJobChannel objects must be set to training, the default value. The ChannelType of the other data source must be set to validation. The two data sources must have the same format, either CSV or Parquet, and the same schema. You must not set the value for the ValidationFraction in this case because all of the data from each source is used for either training or validation. Setting this value causes an error.

CreateAutoMLJob

Each AutoMLChannel object (see the required parameter InputDataConfig) has a ChannelType, which can be set to either training or validation values that specify how the data is to be used when building a machine learning model. At least one data source must be provided and a maximum of two data sources is allowed: one for training data and one for validation data.

How you split the data into training and validation datasets depends on whether you have one or two data sources.

If you only have one data source, the ChannelType is set to training by default and must have this value.
- If the ValidationFraction value in AutoMLDataSplitConfig is not set, 0.2 (20%) of the data from this source is used for validation by default.
- If the ValidationFraction is set to a value between 0 and 1, the dataset is split based on the value specified, where the value specifies the fraction of the dataset used for validation.
If you have two data sources, the ChannelType of one of the AutoMLChannel objects must be set to training, the default value. The ChannelType of the other data source must be set to validation. The two data sources must have the same format, either CSV or Parquet, and the same schema. You must not set the value for the ValidationFraction in this case because all of the data from each source is used for either training or validation. Setting this value causes an error.

For information on split and cross-validation in Autopilot see Cross-validation in Autopilot.

Note

In some cases, Autopilot is unable to infer the ProblemType with high enough confidence, in which case you must provide the value for the job to succeed.

You can add a sample weights column to your tabular dataset and then pass it to your AutoML job to request dataset rows to be weighted during training and evaluation.

Support for sample weights is available in ensembling mode only. Your weights should be numeric and non-negative. Data points with invalid or no weight value are excluded. For more information on the available objective metrics, see Autopilot weighted metrics.

Migrate a CreateAutoMLJob to CreateAutoMLJobV2

We recommend users of CreateAutoMLJob to migrate to CreateAutoMLJobV2.

This section explains the differences in the input parameters between CreateAutoMLJob and CreateAutoMLJobV2 by highlighting the changes in the position, name, or structure of the objects and attributes of the input request between the two versions.

Request attributes that did not change between versions.


{
   "AutoMLJobName": "string",
   "AutoMLJobObjective": { 
      "MetricName": "string"
   },
   "ModelDeployConfig": { 
      "AutoGenerateEndpointName": boolean,
      "EndpointName": "string"
   },
   "OutputDataConfig": { 
      "KmsKeyId": "string",
      "S3OutputPath": "string"
   },
   "RoleArn": "string",
   "Tags": [ 
      { 
         "Key": "string",
         "Value": "string"
      }
   ]
}

Request attributes that changed position and structure between versions.

The following attributes changed position: DataSplitConfig, Security Config, CompletionCriteria, Mode, FeatureSpecificationS3Uri, SampleWeightAttributeName, TargetAttributeName.

The following attributes changed position and structure between versions.

The following JSON illustrates how AutoMLJobConfig.CandidateGenerationConfig of type AutoMLCandidateGenerationConfig moved to AutoMLProblemTypeConfig.TabularJobConfig.CandidateGenerationConfig of type CandidateGenerationConfig in V2.

Request attributes that changed name and structure.

The following JSON illustrates how InputDataConfig (An array of AutoMLChannel) changed to AutoMLJobInputDataConfig (An array of AutoMLJobChannel) in V2. Note that the attributes SampleWeightAttributeName and TargetAttributeName move out of InputDataConfig and into AutoMLProblemTypeConfig.

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Document Conventions

SageMaker Autopilot

Datasets Format and Problem Types