🎯 Course Conclusion

Model compression is not a secondary topic: it is a central discipline in modern AI engineering. Without it, research advances could not be transferred to the real world. Mastering pruning, distillation, and quantization makes you a complete AI engineer: you don’t just know how to build models, but also how to make them viable, efficient, and sustainable.

By the end of this course, you will be able to:

• Choose the appropriate compression technique for each scenario.
• Implement quantization, pruning, and distillation on real models.
• Measure and communicate trade-offs professionally.
• Prepare models for production in resource-constrained environments.

AI is not just about having the largest model. It’s about having the most suitable model.

📚 Additional Resources

• Official documentation:

  • Hugging Face Optimum: https://huggingface.co/docs/optimum/index
  • ONNX Runtime: https://onnxruntime.ai/
  • PyTorch Quantization: https://pytorch.org/docs/stable/quantization.html

• Key papers:

  • “Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference” (Jacob et al., 2017)
  • “Distilling the Knowledge in a Neural Network” (Hinton et al., 2015)
  • “Pruning Neural Networks Without Any Data by Iteratively Conserving Synaptic Flow” (Tan & Le, 2020) — SNIP

• Recommended tools:

  • torch-pruner for structured pruning.
  • TextBrewer for text model distillation.
  • TensorRT for quantization and optimization on NVIDIA GPUs.