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SageMaker Autopilot

Amazon SageMaker Autopilot is an automated machine learning solution (commonly referred to as "AutoML") for tabular datasets. It automatically trains and tunes the best machine learning models for classification or regression based on your data, and hosts a series of models on an Inference Pipeline.

SageMaker AutoML Class

The SageMaker AutoML class is similar to a SageMaker Estimator where you define the attributes of an AutoML job and feed input data to start the job.

Here's a simple example of using the AutoML object:

from sagemaker import AutoML

auto_ml = AutoML(
    role="sagemaker-execution-role",
    target_attribute_name="y",
    sagemaker_session=sagemaker_session,
)
auto_ml.fit(inputs=inputs)

The above code starts an AutoML job (data processing, training, tuning) and outputs a maximum of 500 candidates by default. You can modify the number of output candidates by specifying max_candidates in the constructor. The AutoML job will figure out the problem type (BinaryClassification, MulticlassClassification, Regression), but you can also specify the problem type by setting problem_type in the constructor. Other configurable settings include security settings, time limits, job objectives, tags, etc.

After an AutoML job is done, there are a few things that you can do with the result.

#. Describe the AutoML job: describe_auto_ml_job() will give you an overview of the AutoML job, information includes job name, best candidate, input/output locations, problem type, objective metrics, etc.

#. Get the best candidate: best_candidate() allows you to get the best candidate of an AutoML job. You can view the best candidate's step jobs, inference containers and other information like objective metrics.

#. List all the candidates: list_candidates() gives you all the candidates (up to the maximum number) of an AutoML job. By calling this method, you can view and compare the candidates.

#. Deploy the best candidate (or any given candidate): deploy() by default will deploy the best candidate to an inference pipeline. But you can also specify a candidate to deploy through candidate parameter.

For more information about AutoML parameters, please refer to: https://sagemaker.readthedocs.io/en/stable/automl.html

SageMaker CandidateEstimator Class

The SageMaker CandidateEstimator class converts a dictionary with AutoML candidate information to an object that allows you to re-run the candidate's step jobs.

The simplest re-run is to feed a new dataset but reuse all other configurations from the candidate:

candidate_estimator = CandidateEstimator(candidate_dict)
inputs = new_inputs
candidate_estimator.fit(inputs=inputs)

If you want to have more control over the step jobs of the candidate, you can call get_steps() and construct training/tuning jobs by yourself.

For more information about CandidateEstimator parameters, please refer to: https://sagemaker.readthedocs.io/en/stable/automl.html#sagemaker.automl.candidate_estimator.CandidateEstimator