Introducing SageMaker Core: A new object-oriented Python SDK for Amazon SageMaker

We’re excited to announce the release of SageMaker Core, a new Python SDK from Amazon SageMaker designed to offer an object-oriented approach for managing the machine learning (ML) lifecycle. This new SDK streamlines data processing, training, and inference and features resource chaining, intelligent defaults, and enhanced logging capabilities. With SageMaker Core, managing ML workloads on SageMaker becomes simpler and more efficient. The SageMaker Core SDK comes bundled as part of the SageMaker Python SDK version 2.231.0 and above.

In this post, we show how the SageMaker Core SDK simplifies the developer experience while providing API for seamlessly executing various steps in a general ML lifecycle. We also discuss the main benefits of using this SDK along with sharing relevant resources to learn more about this SDK.

Traditionally, developers have had two options when working with SageMaker: the  AWS SDK for Python, also known as boto3, or the SageMaker Python SDK. Although both provide comprehensive APIs for ML lifecycle management, they often rely on loosely typed constructs such as hard-coded constants and JSON dictionaries, mimicking a REST interface. For instance, to create a training job, Boto3 offers a create_training_job API, but retrieving job details requires the describe_training_job API.

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