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Duration 21 hours (3 days)
Course Outline
Enterprise AI Fundamentals for PostgreSQL
- Defining the role of PostgreSQL within modern AI infrastructure.
- Examining the AI model lifecycle and data pipeline architecture.
- Aligning AI integration with broader enterprise data strategies.
Deploying PostgreSQL for AI Workloads
- Installing PostgreSQL along with necessary AI-related extensions.
- Configuring pgvector and specialized AI processing plugins.
- Optimizing PostgreSQL performance for embedding and inference tasks.
AI Integration Strategies
- Connecting PostgreSQL with models such as Deepseek, Qwen, Mistral Small, and OpenAI.
- Developing RESTful APIs to facilitate interaction between AI services and PostgreSQL.
- Incorporating LLM-driven analytics directly into SQL queries.
Vector Databases and Semantic Intelligence
- Understanding embeddings and the mechanics of vector similarity search.
- Implementing pgvector to enable semantic retrieval capabilities.
- Integrating PostgreSQL with hybrid vector database solutions.
Performance Tuning and Optimization
- Utilizing high-performance indexing and caching strategies for AI-driven queries.
- Managing parallel query execution and workload partitioning.
- Scaling PostgreSQL horizontally for demanding AI applications.
Security, Compliance, and Governance
- Ensuring data lineage and model transparency within PostgreSQL.
- Implementing strict access controls and audit logging for AI data.
- Meeting compliance requirements under GDPR, SOC 2, and ISO 27001 standards.
Automation and Monitoring
- Leveraging AI for proactive database monitoring and anomaly detection.
- Automating SQL query generation and optimization using LLMs.
- Connecting PostgreSQL logs to AI-powered observability platforms.
Enterprise Case Studies and Future Roadmap
- Analysing enterprise-scale deployments combining AI with PostgreSQL.
- Optimizing cost-performance balance in production environments.
- Exploring emerging trends in AI-native relational databases.
Summary and Next Steps
Requirements
- A solid grasp of relational database systems and SQL syntax.
- Practical experience in PostgreSQL administration and development.
- Familiarity with AI/ML model mechanics and data processing workflows.
Target Audience
- Enterprise data architects tasked with integrating AI capabilities into PostgreSQL.
- Engineering leaders overseeing AI-driven database systems.
- Database administrators responsible for managing secure, AI-enabled environments.
