Standard 4 stops to get here

Pruning

Removing unnecessary weights or neurons from a trained model to reduce size and computation while maintaining performance.

Your route here

4 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 Compression ✓ understood

    Techniques to reduce model size and computational requirements (quantization, pruning, distillation) for efficient deployment.

  5. Pruning · you are here ✓ understood

Where it sits

Before this

Model Compression
Pruning

Leads to

Nothing yet: a destination in its own right.

Explore nearby

Shipping AI Quantization Reducing model precision (FP32 → INT8) to decrease size and increase inference speed with minimal accuracy loss. Training Knowledge Distillation Training a smaller 'student' model to mimic a larger 'teacher' model, transferring knowledge while reducing size. Training L1 Regularization Adding the sum of absolute weights to the loss function, promoting sparsity and feature selection. Shipping AI Edge Deployment Running models on edge devices (phones, IoT) rather than cloud servers for lower latency and privacy.