Production ML: When Performance Requirements Beat Accuracy
From the Energy Recommendation System
The Production Reality
In production ML, we optimize for latency, memory, reliability—not just accuracy. Building a grid-scale energy recommendation system, I replaced an end-to-end ensemble stack with a modular, three-stage pipeline that processes 8,000+ buildings in under 30 seconds with <50MB memory usage.
Architecture
- Stage 1: Multi-Cohort Forecasting — LSTM forecasting for 15 building types
- Stage 2: Compliance Prediction — Realistic 36.3% compliance modeling
- Stage 3: Portfolio Optimization — Constraint-based selection for grid impact
Results
- Processing speed: <30s for 8,111 buildings
- Memory usage: <50MB
- Grid reduction: 5.4% aggregate during extreme weather (within 2–7% benchmarks)
Why Modularity Wins
- Enables error isolation and fallback behavior
- Supports parallel development and clear interfaces
- Improves operational reliability under load
Leadership Lessons
- Performance constraints often determine architecture more than pure accuracy
- Realistic assumptions (e.g., compliance rates) build stakeholder credibility
- Monitoring and observability are first-class features in production
Explore the full implementation: energy-recommendation-engine.