Performance Engineering

Optimization Targets

• Frontend: Code splitting, tree shaking, asset compression, avoidance of unnecessary re-renders, bundle size inspection (<500kB chunks).
• Backend: Connection pooling, async concurrency, sub-millisecond in-memory lookups, zero redundant I/O.
• Database: Query explain plans, index tuning, batch inserts, and selective column projection.
• ML / Forecasting: Vectorized NumPy/Pandas operations, lightweight gradient boosted trees or linear baselines, avoiding multi-second inference blocking.