AI Engineering•October 2026•7 min read
Building AI That Actually Works in Production
Bridging the chasm between impressive Jupyter notebook demos and mission-critical 99.99% uptime enterprise software.
CO
Core Architecture
Machine Learning Engineering · NADSCA
MLOpsEnterprise AISystem Architecture
Executive Key Takeaways
- Prototypes succeed in controlled notebooks; production systems must endure messy inputs and network jitter.
- Deterministic business logic must sandwich stochastic neural network inference.
- Latency budgets, fallback caches, and token cost caps are non-negotiable.
1. The 90% Demo Trap
Getting an LLM or vision model to perform well 90% of the time in a demo takes a weekend. Getting it to perform with 99.9% reliability under real user loads takes months of disciplined systems engineering.
2. Guardrails and Deterministic Fallbacks
Never feed unstructured raw model outputs directly into transactional databases. Strict JSON Schema enforcement, circuit breakers, and rate-limiting fallbacks are critical.
Next Steps
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