When setting a problem type, such as binary classification or regression, with the AutoML API, you have the option of specifying it or of letting Amazon SageMaker Autopilot detect it on your behalf. You set the type of problem with the CreateAutoPilot.ProblemType

Use SageMaker Debugger to create output tensor files that are compatible with TensorBoard. Load the files to visualize in TensorBoard and analyze your SageMaker training jobs.

SageMaker provides prebuilt Docker images that install the scikit-learn and Spark ML libraries. These libraries also include the dependencies needed to build Docker images that are compatible with SageMaker using the Amazon SageMaker Python SDK . With the SDK, you can use scikit-learn for machine learning tasks and use Spark ML to create and tune machine learning pipelines. For instructions on installing and using the SDK, see

Learn how to use the different APIs in Amazon Augmented AI, including the control plane and data plane, and the direct integrations with Amazon Rekognition and Amazon Textract.

After you create a project, view the resources associated with the project in SageMaker Studio.

Amazon SageMaker Ground Truth has several built-in task types. Ground Truth provides a worker task template for buit-in task types. Additionally, some built in task types support . The following topics describe each built-in task type and demo the worker task templates that are provided by Ground Truth in the console. To learn how to create a labeling job in the console using one of these task types, select the task type page.

A workforce is the group of workers that you have selected to label your dataset. You can choose either the Amazon Mechanical Turk workforce, a vendor-managed workforce, or you can create your own private workforce to label or review your dataset. Whichever workforce type you choose, Amazon SageMaker takes care of sending tasks to workers.

If you choose, Amazon SageMaker Ground Truth can use active learning to automate the labeling of your input data for certain built-in task types. Active learning is a machine learning technique that identifies data that should be labeled by your workers. In Ground Truth, this functionality is called automated data labeling. Automated data labeling helps to reduce the cost and time that it takes to label your dataset compared to using only humans. When you use automated labeling, you incur SageMaker training and inference costs.

Amazon SageMaker automatically creates tracking entities for SageMaker jobs, models, model packages, and endpoints if the data is available. There is no limit to the number of lineage entities automatically created by SageMaker.

Amazon SageMaker Debugger is available for any deep learning models that you bring to Amazon SageMaker. The AWS CLI, SageMaker Estimator API, and the Debugger APIs enable you to use any Docker base images to build and customize containers to train your models. To use Debugger with customized containers, you need to make a minimal change to your training script to implement the Debugger hook callback and retrieve tensors from training jobs.