Python utilities for compliant Azure machine learning

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Project description

Shrike: Compliant Azure ML Utilities

CodeQL docs python Component Governance ci-gate Python versions code style: black codecov license: MIT

Compliant Machine Learning is the practice of training, validating and deploying machine learning models withou seeing the private data. It is needed in many enterprises to satsify the strict compliance and privacy guarantees that they provide to their customers.

The library shrike is a set of Python utilities for compliant machine learning, with a special emphasis on running pipeline in the platform of Azure Machine Learning. This library mainly contains three components, that are

  • shrike.confidential_logging: utlities for confidential logging and exception handling;
  • shrike.pipeline: helper code for manging, validating and submitting Azure Machine Learning pipelines based on azure-ml-component;
  • shrike.build: helper code for packaging, building, validating, signing and registering Azure Machine Learning components.

Documentation

For the full documentation of shrike with detailed examples and API reference, please see the docs page.

Installation

To install via PyPi, please type:

pip install shrike[pipeline,build]

There are three optional extra dependenciies - pipeline, build and dev, among which dev is for the development environment of shrike. If only the confidential-logging feature would be used, please just type without any extras:

pip install shrike

Need Support?

When you have any feature requests or technical questions or find any bugs, please don't hesitate to contact the Azure ML Data Science Team.

  • If you are Microsoft employees, please refer to the support page for details;
  • If you are outside Microsoft, feel free to send an email to aml-ds@microsoft.com.

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.

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