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