MODULE MAP

Arrive at Terra Grid HQ and discover why ML models must run on remote edge devices with 512MB RAM, ARM processors, and intermittent connectivity. You'll build your first edge anomaly detector using concepts you already know from frontend development.

Your model uses 1.8GB RAM but edge devices only have 512MB. Learn to profile, benchmark, and plan model optimization — translating frontend performance budgeting into ML model compression strategy.

Shrink your model from Float32 to Int8 — 4x smaller with only 1.2% accuracy loss. Master quantization techniques that map directly to image compression and Webpack minification concepts you already use.

A massive model that knows everything needs to become a tiny model that knows just enough. Learn pruning and knowledge distillation — the ML equivalents of tree-shaking and code splitting.

Tariq deploys the Terra Grid Intelligence model to 847 remote edge devices across Sector 7 — over satellite links that cost $0.50 per MB. You'll learn to package, ship, and update ML models on constrained hardware using the same mental models as npm publishing and Service Worker updates.

Manual retraining doesn't scale when you have 12 sectors generating new sensor data every hour. Priya builds automated ML pipelines with human oversight at the gates — the same CI/CD thinking you use for frontend deployments, applied to model training and delivery.

Region 4 accuracy degrades from 96.2% to 89.1% over 14 days — a sensor firmware update changed the data distribution. Hassan shows why production monitoring and drift detection are non-negotiable, using the same alerting and regression testing concepts you know from frontend observability.

Power companies won't share sensor data — regulatory and competitive reasons make centralized training impossible. Elif and Hassan bring the training to the data using federated learning, the ML equivalent of respecting CORS boundaries while still building a shared model.

Hassan's rule: no model goes to 100% of devices without proof it's better. After a 2024 drift incident cost $4.2 million, Terra Grid adopted rigorous A/B testing for every model update. Traffic splitting, statistical significance, canary deployments, and multi-armed bandits — the same strategies you use for frontend feature flags, applied to edge ML models.

Stress test simulation. GRID simulates simultaneous sensor anomalies across 3 regions — 12,400 affected devices. Hassan's philosophy: everything fails eventually; the question is whether it fails well. Scaling strategies, model sharding, failover systems, load management, and global operations — building ML infrastructure that survives the worst day.

Full deployment across 47 sites, 12,400 devices. Two days later: a cascading anomaly at Substation 7-Alpha triggers chain reactions across 3 regions. Terra Grid Intelligence identifies the root cause in 11.7 seconds — manual analysis would take 2-3 hours. This is what it looks like when the system works.