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.compliant_logging: utlities for compliant 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.
For the full documentation of shrike with detailed examples and API reference,
please see the docs page.
The library shrike is publicly available in PyPi. There are three optional extra dependenciies - pipeline, build and dev,
among which pipeline is for submitting Azure Machine Learning pipelines, build is for signing and registering components,
and dev is for the development environment of shrike.
- If only the compliant-logging feature would be used, please pip install without any extras:
pip install shrike- If it will be used for signing and registering components, please type with
[build]:
pip install shrike[build]- If it will be used for submitting Azure Machine Learning pipelines, please type with
[pipeline]:
pip install shrike[pipeline]- If you would like to contribute to the source code, please install with all the dependencies:
pip install shrike[pipeline,build,dev]If you have been using "aml-build-tooling", "aml-ds-pipeline-contrib", and confidential-ml-utils libraries, please use the migration script (migration.py) to convert your repo or file and adopt the shrike package with one simple command:
python migraton.py --input_path PATH/TO/YOUR/REPO/OR/FILEWhen you have any feature requests or technical questions or find any bugs, please don't hesitate to file issues.
- 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.
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.
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