description: "Build professional profiling infrastructure to measure and analyze performance.\n\
  Students learn to create timing, memory, and operation profilers that reveal\nbottlenecks\
  \ and guide optimization decisions. Performance detective work that \nmakes optimization\
  \ exciting through data-driven insights.\n"
difficulty: advanced
estimated_hours: 8-10
exports:
- tinytorch.profiling
learning_objectives:
- Build accurate timing infrastructure with statistical rigor
- Implement memory profiling and allocation tracking
- Create FLOP counting for computational analysis
- Master profiling methodology for bottleneck identification
- Connect profiling insights to ML systems optimization decisions
name: Profiling
number: 15
prerequisites:
- Module 14: Transformers (need models to profile)
skills_developed:
- Performance measurement
- Bottleneck identification
- Profiling tool development
- Statistical analysis
type: systems