README

!SageMaker

 :exclamation::fire: Announcing SageMaker-Core: A New Python SDK for Amazon SageMaker :fire::exclamation:

 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.


 Amazon SageMaker Examples

Example Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using Amazon SageMaker.

 :books: Read this before you proceed further

Amazon SageMaker examples are divided in two repositories:

- SageMaker example notebooks is the official repository, containing examples that demonstrate the usage of Amazon SageMaker. This repository is entirely focussed on covering the breadth of features provided by SageMaker, and is maintained directly by the Amazon SageMaker team.

- Sagemaker Example Community repository is another SageMaker repository which contains additional examples and reference solutions, beyond the examples showcased in the official repository. This repository is maintained by community of engineers and solution architects at AWS.

 Planning to submit a PR to this repository? Read this first:

- This repository will only accept notebooks/examples which demonstrate a feature of SageMaker, not yet covered anywhere in this repository. PR submitters are requested to check this before submitting the PR to avoid getting it rejected.

- If you still would like to contribute your example, please submit a PR to Sagemaker Example Communi