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Duration 21 hours
Course Outline
Enterprise AI Fundamentals for PostgreSQL
- Defining PostgreSQL's role in contemporary AI infrastructure
- Overview of the AI model lifecycle and data pipeline architecture
- Aligning AI integration with broader enterprise data strategies
Deploying PostgreSQL for AI Workloads
- Setting up PostgreSQL along with essential AI extensions
- Configuration of pgvector and related AI processing plugins
- Tuning PostgreSQL performance for efficient embeddings and inference
AI Integration Strategies
- Connecting PostgreSQL with Deepseek, Qwen, Mistral Small, and OpenAI
- Developing RESTful APIs to facilitate AI-PostgreSQL interaction
- Incorporating LLM-driven analytics directly into SQL queries
Vector Databases and Semantic Intelligence
- Exploring the concepts of embeddings and vector similarity search
- Applying pgvector to achieve semantic retrieval capabilities
- Connecting PostgreSQL with hybrid vector database solutions
Performance Tuning and Optimization
- Enhancing high-performance indexing and caching for AI-driven queries
- Implementing parallel query execution and workload partitioning
- Achieving horizontal scaling of PostgreSQL within AI applications
Security, Compliance, and Governance
- Ensuring data lineage and model transparency within PostgreSQL
- Managing access control and audit logging for AI-related data
- Adhering to GDPR, SOC 2, and ISO 27001 compliance standards
Automation and Monitoring
- Leveraging AI for database monitoring and anomaly detection
- Utilizing LLMs to automate SQL query generation and optimization
- Integrating PostgreSQL logs with AI-powered observability platforms
Enterprise Case Studies and Future Roadmap
- Examining enterprise-scale AI deployments using PostgreSQL
- Optimizing cost-performance ratios in production environments
- Exploring emerging trends in AI-native relational databases
Summary and Next Steps
Requirements
- Foundational knowledge of relational database systems and SQL syntax
- Practical experience in PostgreSQL administration and development
- Proficiency in AI/ML model concepts and data processing workflows
Target Audience
- Enterprise data architects focused on integrating AI with PostgreSQL
- Engineering leads overseeing AI-driven database systems
- Database administrators responsible for managing secure, AI-enabled environments
