Create an AutoML job for text classification using the API - Amazon SageMaker
Required parametersOptional parameters

Create an AutoML job for text classification using the API

The following instructions show how to create an Amazon SageMaker Autopilot job as a pilot experiment for text classification problem types using SageMaker API Reference.

Note

Tasks such as text and image classification, time-series forecasting, and fine-tuning of large language models are exclusively available through the version 2 of the AutoML REST API. If your language of choice is Python, you can refer to AWS SDK for Python (Boto3) or the AutoMLV2 object of the Amazon SageMaker Python SDK directly.

Users who prefer the convenience of a user interface can use Amazon SageMaker Canvas to access pre-trained models and generative AI foundation models, or create custom models tailored for specific text, image classification, forecasting needs, or generative AI.

You can create an Autopilot text classification experiment programmatically by calling the CreateAutoMLJobV2 API action in any language supported by Amazon SageMaker 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).

The following is a collection of mandatory and optional input request parameters for the CreateAutoMLJobV2 API action used in text classification.

Required parameters

When calling CreateAutoMLJobV2 to create an Autopilot experiment for text classification, you must provide the following values:

All other parameters are optional.

Optional parameters

The following sections provide details of some optional parameters that you can pass to your text classification AutoML job.

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

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.

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.

To enable automatic deployment for the best model candidate of an AutoML job, include a ModelDeployConfig in the AutoML job request. This will allow the deployment of the best model to a SageMaker endpoint. Below are the available configurations for customization.