Model Optimization Techniques

Strategies and code examples for improving model efficiency and performance

Why Optimize AI Models?

Model optimization improves computational efficiency, reduces resource usage, and increases deployment speed while maintaining accuracy. This tutorial covers key techniques including hyperparameter tuning, quantization, and pruning.

  • Reduce inference time by up to 70%
  • Decrease model size without accuracy loss
  • Optimize for mobile/cloud deployment

Hyperparameter Tuning

const optimizer = new HyperparameterOptimizer({
  model: 'vision-transformer',
  search_space: {
    learning_rate: [0.001, 0.0001],
    batch_size: [32, 64, 128],
    epochs: [10, 20, 50]
  },
  metric: 'val_accuracy',
  num_trials: 20
});

optimizer.search(training_data).then(best_model => {
  // Use optimized model
});

Use automated search algorithms to find optimal parameter configurations without manual trial.

Model Quantization

const quantized_model = quantize(model, {
  bits: 8,
  method: 'dynamic_quant',
  output_path: 'quantized-model.onnx'
});

Reduce model size by converting from 32-bit floats to 8-bit integers while maintaining performance.

Model Pruning

const pruned_model = prune(model, {
  sparsity: '80%',
  layers: ['dense', 'conv']
});

Remove redundant weights to decrease computational load by 60-90% with minimal accuracy loss.

Knowledge Distillation

const distiller = new KnowledgeDistiller({
  teacher_model: largeModel,
  student_model: compactModel,
  temperature: 3
});

distiller.distill(train_data);

Leverages knowledge from large models to train smaller, efficient student models.

Best Practices

Start Simple

Begin with hyperparameter tuning before complex optimizations.

Profile First

Use model analyzers to identify optimization bottlenecks.

Incremental Testing

Apply optimizations in stages and validate results.

Need Optimization Help?

Our optimization specialists can help with complex model compression and performance tuning

Contact Optimization Team