Further reading

This section offers a list of useful web resources that will help you augment your knowledge of the Azure Machine Learning SDK and the various code snippets that were used in this chapter:

• The SmartNoise library for differential privacy: https://github.com/opendp/smartnoise-core
• HE resources: https://www.microsoft.com/en-us/research/project/homomorphic-encryption/
• Deploying an encrypted inference web service: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-homomorphic-encryption-seal
• Presidio, the data protection and anonymization API: https://github.com/Microsoft/presidio
• Sample repository for aDevOps process in data science projects, also known as MLOps: https://aka.ms/mlOps
• Model Cards for Model Reporting: https://arxiv.org/pdf/1810.03993.pdf
• The InterpretML website, with links to the GitHub repository of the community: https://interpret.ml/
• The Error Analysis home page, including guides on how to use the toolkit...