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Demos and Tutorials

This section lists several demos that apply Concrete ML to some popular machine learning problems. They show how to build ML models that perform well under FHE constraints, and then how to perform the conversion to FHE.
Simpler tutorials that discuss only model usage and compilation are also available for built-in models and deep learning.
GPT-2 in FHE
Privacy-preserving text generation based on a user's prompt
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Titanic
Train an XGB classifier that can perform encrypted prediction for the Kaggle Titanic competition​
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Federated Learning and Private Inference
Use federated learning to train a Logistic Regression while preserving training data confidentiality. Import the model into Concrete ML and perform encrypted prediction
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Neural Network Fine-tuning
Fine-tune a VGG network to classify the CIFAR image data-sets and predict on encrypted data
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Neural Network Splitting for SaaS deployment
Train a VGG-like CNN that classifies CIFAR10 encrypted images, and where an initial feature extractor is executed client-side
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Encrypted Image filtering
A Hugging Face space that applies a variety of image filters to encrypted images
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Encrypted sentiment analysis
A Hugging Face space that securely analyzes the sentiment expressed in a short text
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Credit Scoring
Predict the chance of a given loan applicant defaulting on loan repayment
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Healthcare diagnosis
Give a diagnosis using FHE to preserve the privacy of the patient
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Getting Started - Previous
Inference in the Cloud
Next - Built-in Models
Linear Models
Last modified 1mo ago