Neural Architecture Search
Automated methods for designing and optimizing neural network structures to improve model performance while balancing complexity and resource constraints.
Uses search algorithms (reinforcement learning, evolutionary strategies, Bayesian optimization) to explore millions of possible layer types, sizes, and connections, discovering architectures that outperform manually designed models. Governance must control compute budgets, track reproducibility of discovered architectures, enforce fairness and efficiency constraints, and validate that NAS-generated models meet interpretability and deployment requirements.
A vision-AI team employs NAS to find an optimized CNN for defect detection on the assembly line. After a 1,000-trial search limited by a FLOP budget, NAS yields a lean architecture achieving 1% higher accuracy and 30% fewer parameters than the baseline. The team logs the NAS configuration and final model for reproducibility in their registry.
“What used to take weeks of manual reviews and policy work is now structured and auditable in Enzai within minutes. It’s the first time AI governance has felt operational, not theoretical.”
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What problem does Enzai solve?
Enzai provides enterprise-grade infrastructure to manage AI risk and compliance. It creates a centralized system of record where AI systems, models, datasets, and governance decisions are documented, assessed, and auditable.
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