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코스 개요
Enterprise AI Fundamentals for PostgreSQL
- Positioning PostgreSQL in modern AI infrastructure
- AI model lifecycle and data pipeline architecture
- Integrating AI with enterprise data strategy
Deploying PostgreSQL for AI Workloads
- Installing PostgreSQL and required AI extensions
- Configuring pgvector and AI processing plugins
- Optimizing PostgreSQL for embedding and inference performance
AI Integration Strategies
- Connecting PostgreSQL with Deepseek, Qwen, Mistral Small, and OpenAI
- Building RESTful APIs for AI-PostgreSQL interaction
- Embedding LLM-driven analytics directly in SQL queries
Vector Databases and Semantic Intelligence
- Understanding embeddings and vector similarity search
- Implementing pgvector for semantic retrieval
- Integrating PostgreSQL with hybrid vector databases
Performance Tuning and Optimization
- High-performance indexing and caching for AI-driven queries
- Parallel query execution and workload partitioning
- Scaling PostgreSQL horizontally in AI applications
Security, Compliance, and Governance
- Data lineage and model transparency in PostgreSQL
- Access control and audit logging for AI data
- Compliance with GDPR, SOC 2, and ISO 27001 standards
Automation and Monitoring
- Using AI for database monitoring and anomaly detection
- Automating SQL query generation and optimization with LLMs
- Integrating PostgreSQL logs with AI-powered observability platforms
Enterprise Case Studies and Future Roadmap
- Enterprise-scale deployments of AI with PostgreSQL
- Cost-performance optimization in production environments
- Emerging trends in AI-native relational databases
Summary and Next Steps
요건
- An understanding of relational database systems and SQL
- Experience with PostgreSQL administration and development
- Familiarity with AI/ML models and data processing workflows
Audience
- Enterprise data architects integrating AI with PostgreSQL
- Engineering leads responsible for AI-driven database systems
- Database administrators managing secure AI-enabled environments
21 시간
