Enterprise AI Architecture
Design scalable, secure AI infrastructure for enterprises: hybrid deployment, data governance, model management, and integration.
TL;DR
Enterprise AI architecture balances cloud and on-prem, enforces data governance, manages model lifecycle, and integrates with existing systems securely and at scale.
Architecture components
Model serving layer: API gateway, load balancing, caching
Data layer: Vector DBs, data lakes, governance
Orchestration: Workflow engines, job scheduling
Monitoring: Observability, logging, alerting
Security: Authentication, authorization, encryption
Deployment patterns
Hybrid cloud: Sensitive data on-prem, less sensitive in cloud
Multi-cloud: Avoid vendor lock-in, geographic distribution
On-prem only: Maximum control and compliance
Data governance
- Data classification (public, internal, confidential)
- Access controls
- Audit trails
- Data lineage tracking
- Compliance (GDPR, CCPA)
Model management
- Model registry (versioning, metadata)
- A/B testing infrastructure
- Rollback capabilities
- Performance monitoring
- Retraining pipelines
Integration
- API layer for existing applications
- SSO and identity management
- Enterprise search integration
- CRM, ERP connectors
Scalability
- Auto-scaling model servers
- Caching layers
- Rate limiting
- Geographic distribution
Security best practices
- Zero-trust architecture
- Encryption in transit and at rest
- Secret management
- Regular security audits
- Incident response plans
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Key Terms Used in This Guide
Model
The trained AI system that contains all the patterns it learned from data. Think of it as the 'brain' that makes predictions or decisions.
AI (Artificial Intelligence)
Making machines perform tasks that typically require human intelligenceālike understanding language, recognizing patterns, or making decisions.
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