7 hours ago
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[center]=7AMAZON VM 2026[/center]
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AMAZON VM 2026
Announcing SageMaker-Core: A New Python SDK for Amazon SageMaker
Introduction
Today, Amazon SageMaker is excited to announce the release of SageMaker-Core, a new Python SDK that provides an object-oriented interface for interacting with SageMaker resources such as TrainingJob, Model, and Endpoint. This SDK introduces the resource chaining feature, allowing developers to pass resource objects as parameters, eliminating manual parameter specification and simplifying code management. SageMaker-Core abstracts low-level details like resource state transitions and polling logic, achieving full parity with SageMaker APIs. It also includes usability improvements such as auto code completion, comprehensive documentation, and type hints, enhancing the overall developer experience.
Use Case
SageMaker-Core is ideal for ML practitioners who seek full customization of AWS primitives for their ML workloads. SageMaker-Core is an improvement over Boto3, providing a more intuitive and efficient way to manage SageMaker resources. By providing an intuitive object-oriented interface and resource chaining, the SDK allows for seamless integration and management of SageMaker resources. This flexibility, combined with intelligent defaults enables developers to tailor their ML workloads according to their needs. Comprehensive documentation, and type hints help developers write code faster and with fewer errors without navigating complex API documentation.
Call to Action
To learn more about SageMaker-Core, visit the documentation and example notebooks. Get started today by integrating SageMaker-Core into your machine learning workflows and experience the benefits of a streamlined and efficient development process.
=7Download Link
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Setup
The quickest setup to run example notebooks includes:
An AWS account
Proper IAM User and Role setup
An Amazon SageMaker Notebook Instance
An S3 bucket
Usage
These example notebooks are automatically loaded into SageMaker Notebook Instances. They can be accessed by clicking on the SageMaker Examples tab in Jupyter or the SageMaker logo in JupyterLab.
Although most examples utilize key Amazon SageMaker functionality like distributed, managed training or real-time hosted endpoints, these notebooks can be run outside of Amazon SageMaker Notebook Instances with minimal modification (updating IAM role definition and installing the necessary libraries).
[center]![[Image: images?q=tbn:ANd9GcT2BDndjUyfm5m3Ruk1Fbh...wc_LDFrQ&s]](https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcT2BDndjUyfm5m3Ruk1FbhOMIgv8owc_LDFrQ&s)
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=7Download Link
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[/center][center]=7AMAZON VM 2026[/center]
[center]
AMAZON VM 2026
Announcing SageMaker-Core: A New Python SDK for Amazon SageMaker
Introduction
Today, Amazon SageMaker is excited to announce the release of SageMaker-Core, a new Python SDK that provides an object-oriented interface for interacting with SageMaker resources such as TrainingJob, Model, and Endpoint. This SDK introduces the resource chaining feature, allowing developers to pass resource objects as parameters, eliminating manual parameter specification and simplifying code management. SageMaker-Core abstracts low-level details like resource state transitions and polling logic, achieving full parity with SageMaker APIs. It also includes usability improvements such as auto code completion, comprehensive documentation, and type hints, enhancing the overall developer experience.
Use Case
SageMaker-Core is ideal for ML practitioners who seek full customization of AWS primitives for their ML workloads. SageMaker-Core is an improvement over Boto3, providing a more intuitive and efficient way to manage SageMaker resources. By providing an intuitive object-oriented interface and resource chaining, the SDK allows for seamless integration and management of SageMaker resources. This flexibility, combined with intelligent defaults enables developers to tailor their ML workloads according to their needs. Comprehensive documentation, and type hints help developers write code faster and with fewer errors without navigating complex API documentation.
Call to Action
To learn more about SageMaker-Core, visit the documentation and example notebooks. Get started today by integrating SageMaker-Core into your machine learning workflows and experience the benefits of a streamlined and efficient development process.
=7Download Link
[/center]
Setup
The quickest setup to run example notebooks includes:
An AWS account
Proper IAM User and Role setup
An Amazon SageMaker Notebook Instance
An S3 bucket
Usage
These example notebooks are automatically loaded into SageMaker Notebook Instances. They can be accessed by clicking on the SageMaker Examples tab in Jupyter or the SageMaker logo in JupyterLab.
Although most examples utilize key Amazon SageMaker functionality like distributed, managed training or real-time hosted endpoints, these notebooks can be run outside of Amazon SageMaker Notebook Instances with minimal modification (updating IAM role definition and installing the necessary libraries).
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=7Download Link
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