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.

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.

Describes the machine learning environments supported by Amazon SageMaker.

The Amazon SageMaker Python SDK Chainer estimators and models and the Amazon SageMaker open-source Chainer container support using the Chainer machine learning framework for training and deploying models in SageMaker.

The following are common errors that you might run into when using Docker containers with SageMaker. Each error is followed by a solution to the error.

You can adapt an existing Docker image to work with SageMaker. You may need to use an existing, external Docker image with SageMaker when you have a container that satisfies feature or safety requirements that are not currently supported by a prebuilt SageMaker image. There are two toolkits that allow you to bring your own container and adapt it to work with SageMaker:

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

Amazon SageMaker Asynchronous Inference is a new capability in SageMaker that queues incoming requests and processes them asynchronously. This option is ideal for requests with large payload sizes (up to 1GB), long processing times (up to 15 minutes), and near real-time latency requirements. Asynchronous Inference enables you to save on costs by autoscaling the instance count to zero when there are no requests to process, so you only pay when your endpoint is processing requests.

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.

Amazon SageMaker Neo supports common machine learning frameworks, edge devices, operating systems, and chip architectures. Find out if Neo supports your framework, edge device, OS, and chip architecture by selecting one of the topics below.