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The AWS Step Functions Data Science SDK is an open-source library that allows data scientists to easily create workflows that process and publish machine learning models using Amazon SageMaker and AWS Step Functions. You can create machine learning workflows in Python that orchestrate AWS infrastructure at scale, without having to provision and integrate the AWS services separately. The best way to quickly review how the AWS Step Functions Data Science SDK works is to review the related example notebooks. These notebooks provide code and descriptions for creating and running workflows in AWS Step Functions Using the AWS Step Functions Data Science SDK. In Amazon SageMaker, example Jupyter notebooks are available in the example notebooks portion of a notebook instance. To run the AWS Step Functions Data Science SDK example notebooks locally, download the sample notebooks and open them in a working Jupyter instance.
Features
- Easily construct and run machine learning workflows that use AWS infrastructure directly in Python
- Instantiate common training pipelines
- Create standard machine learning workflows in a Jupyter notebook from templates
- The AWS Step Functions Data Science SDK is built to PyPI and can be installed with pip
- The AWS Step Functions Data Science SDK supports Unix/Linux and Mac
- The AWS Step Functions Data Science SDK is tested on Python 3.6
Project Samples
