LEVEL 4

Optimizing AI at Scale

Handle 675M users with 99.9% uptime like Spotify's global infrastructure

Originally inspired by Zach Wilson (@eczachly)'s insights on AI Engineering levels

675M
Users Served
99.9%
Uptime
10ms
Edge Latency

Master Enterprise-Scale AI

🌐

Distributed Inference

vLLM, Ray Serve, TensorRT-LLM. Handle millions of concurrent requests with 1.7x speedup and 4x lower latency.

📱

Edge Deployment

Sub-10ms responses like Mercedes-Benz safety systems. TensorFlow Lite and 92% GPU requirement reduction.

💰

Cost Optimization

90% savings with serverless deployment. Spot instances, quantization, and smart model selection strategies.

🔒

Enterprise Compliance

GDPR, HIPAA, SOC2 compliance. PII protection, audit logs, and data governance for regulated industries.

What You'll Build

Week 25-26: Distributed System

Multi-GPU inference with vLLM. Auto-scaling, load balancing, and fault tolerance for enterprise workloads.

🧠

Week 27-28: Memory Optimization

Context compression, quantization, gradient checkpointing. Handle 32k+ context windows efficiently.

📊

Week 29-30: Cost Dashboard

Real-time monitoring, budget alerts, ROI analysis. Track every dollar spent on AI infrastructure.

🛡️

Week 31-32: Compliance System

End-to-end privacy protection, audit trails, and regulatory compliance for enterprise deployment.

Ready to Scale AI?

Build systems that serve millions with enterprise reliability

View Full Curriculum Teaching Manual →