Reference 5 stops to get here

Model Extraction

Stealing a model's functionality by querying it and training a copy.

Your route here

5 stops · basics first
  1. Dataset ✓ understood

    A collection of data examples used for training, validating, or testing machine learning models.

  2. Training ✓ understood

    The process of fitting a model to data by repeatedly measuring how wrong its outputs are and adjusting its parameters to reduce that error.

  3. Inference ✓ understood

    Running a trained model on new inputs to get predictions, with its weights frozen: the stage of a model's life that users actually interact with.

  4. Model Serving ✓ understood

    Deploying trained models as services that can handle prediction requests in production environments.

  5. Model Endpoint ✓ understood

    A deployed service exposing a model's predictions via API requests.

  6. Model Extraction · you are here ✓ understood

Stealing a model’s functionality by querying it and training a copy.

This concept is essential for understanding specialized ai topics and forms a key part of modern AI systems.

  • Security
  • Attack
  • Model Stealing

Where it sits

Before this

Model Endpoint
Model Extraction

Leads to

Nothing yet: a destination in its own right.

Explore nearby

Shipping AI Model Inversion Attacks that reconstruct training data or private information from model parameters or outputs. Shipping AI Membership Inference Determining if a specific example was in the training dataset, a privacy concern. Shipping AI Model Watermarking Embedding identifiable signatures in models to prove ownership and detect theft. Training Knowledge Distillation Training a smaller 'student' model to mimic a larger 'teacher' model, transferring knowledge while reducing size.