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17. Quantization
Reducing Model Size Without Losing Accuracy
Quantization is a critical technique for deploying ML models in production, especially on edge devices. In this module, you'll learn how to reduce model size and increase inference speed by converting floating-point weights to lower precision formats.
What You'll Build
- INT8 Quantization: Convert 32-bit floats to 8-bit integers
- Quantization-Aware Training: Train models that quantize well
- Dynamic Quantization: Quantize activations at runtime
- Static Quantization: Pre-compute quantization parameters
Why This Matters
Modern ML models are often too large for deployment:
- GPT models can be hundreds of gigabytes
- Mobile devices have limited memory
- Edge computing requires efficient models
- Quantization can reduce model size by 75% with minimal accuracy loss
Learning Objectives
By the end of this module, you will:
- Understand the trade-offs between model size and accuracy
- Implement INT8 quantization from scratch
- Build quantization-aware training pipelines
- Measure the impact on model performance
Prerequisites
Before starting this module, you should have completed:
- Module 02: Tensor (for basic operations)
- Module 04: Layers (for model structure)
- Module 08: Training (for fine-tuning quantized models)
Real-World Applications
Quantization is used everywhere in production ML:
- Mobile Apps: TensorFlow Lite uses INT8 for on-device inference
- Edge Devices: Raspberry Pi and Arduino deployment
- Cloud Inference: Reducing serving costs at scale
- Neural Processors: Apple Neural Engine, Google Edge TPU
Coming Up Next
After mastering quantization, you'll explore:
- Module 18: Compression - Further model size reduction techniques
- Module 19: Caching - Optimizing inference latency
- Module 20: Benchmarking - Measuring the impact of optimizations
This module is currently under development. The implementation will cover practical quantization techniques used in production ML systems.