BigDL-PPML
=========================

Protecting privacy and confidentiality is critical for large-scale data analysis and machine learning. BigDL PPML (BigDL Privacy Preserving Machine Learning) combines various low-level hardware and software security technologies (e.g., Intel® Software Guard Extensions (Intel® SGX), Security Key Management, Remote Attestation, Data Encryption, Federated Learning, etc.) so that users can continue applying standard Big Data and AI technologies (such as Apache Spark, Apache Flink, TensorFlow, PyTorch, etc.) without sacrificing privacy.

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        **Get Started**
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        Documents in these sections helps you getting started quickly with PPML.

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        :bdg-link:`Installation <./Overview/install.html>` |
        :bdg-link:`Hello World Example  <./Overview/quicktour.html>`


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        **User Guide**
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        Provides you with in-depth information about PPML features and concepts and step-by-step guides.

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        :bdg-link:`Introduction <./Overview/intro.html>` |
        :bdg-link:`Advanced Topics <./Overview/misc.html>`


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        **Tutorials**
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        PPML Tutorials and Examples.

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        :bdg-link:`End-to-End Example <./Overview/examples.html>` |
        :bdg-link:`More Examples <https://github.com/intel-analytics/BigDL/blob/main/ppml/docs/examples.md>`

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        **Videos**
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        Videos and Demos helps you quick understand the architecture and start hands-on work.

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        :bdg-link:`Introduction <./Overview/intro.html#what-is-bigdl-ppml>` |
        :bdg-link:`E2E Workflow <./QuickStart/end-to-end.html#e2e-architecture-overview>` |
        :bdg-link:`E2E Demo <./QuickStart/end-to-end.html#video-demo>`






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    BigDL-PPML Document <self>