Standard 3 stops to get here · leads to 1
Model Compression
Techniques to reduce model size and computational requirements (quantization, pruning, distillation) for efficient deployment.
Your route here
3 stops · basics first
- Dataset ✓ understood
A collection of data examples used for training, validating, or testing machine learning models.
- 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.
- 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.
- Model Compression · you are here ✓ understood
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Shipping AI Quantization Reducing model precision (FP32 → INT8) to decrease size and increase inference speed with minimal accuracy loss. Shipping AI Pruning Removing unnecessary weights or neurons from a trained model to reduce size and computation while maintaining performance. Training Knowledge Distillation Training a smaller 'student' model to mimic a larger 'teacher' model, transferring knowledge while reducing size. Shipping AI Edge Deployment Running models on edge devices (phones, IoT) rather than cloud servers for lower latency and privacy. Training LoRA Low-Rank Adaptation - a parameter-efficient fine-tuning method that updates only small low-rank matrices instead of full weights.