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Model Compression for Production AI Systems

7 chapters · 33 lessons

1. 1. Profiling Models for Compression Opportunities

   4 lessons

   Measure model behavior, identify redundancy, and establish baseline metrics before compression.

2. 2. Quantization Techniques and Precision Tradeoffs

   5 lessons

   Reduce weight precision while preserving model capability for target tasks.

3. 3. Pruning and Sparsity for Structural Reduction

   5 lessons

   Remove weights, neurons, and attention heads that contribute minimally to performance.

4. 4. Knowledge Distillation for Compact Models

   4 lessons

   Train smaller student models to reproduce larger teacher model behavior.

5. 5. Empirical Evaluation for Compressed Models

   5 lessons

   Test actual deployment tasks beyond benchmark scores to validate compression quality.

6. 6. Matching Model Size to Deployment Constraints

   5 lessons

   Make architecture decisions that balance footprint, accuracy, and user experience for specific workloads.

7. 7. Building Production Compression Pipelines

   5 lessons

   Automate compression workflows from profiling through deployment validation.