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코스 개요
Introduction to AI in Postgres
- Overview of AI and data-driven systems
- AI use cases within Postgres environments
- Architecture considerations for AI workloads
Setting Up the Environment
- Installing PostgreSQL and configuring pgvector
- Setting up Python for AI integrations
- Connecting Postgres to local and cloud-based LLMs
AI Extensions and Vector Databases
- Understanding vector embeddings in Postgres
- Using pgvector for similarity search and semantic queries
- Benchmarking AI extensions vs. external vector stores
Integrating LLMs with Postgres
- Connecting Postgres with OpenAI, Deepseek, Qwen, and Mistral Small
- Designing AI query pipelines
- Storing and retrieving embeddings efficiently
Building Intelligent Query Systems
- Natural language to SQL using LLMs
- Automating query generation and optimization
- AI-assisted database search and summarization
Optimizing Postgres for AI Workloads
- Indexing strategies for embeddings
- Performance tuning and caching for AI queries
- Scaling Postgres with distributed and cloud architectures
Security and Governance in AI-Enabled Databases
- Data privacy and compliance considerations
- Managing API keys and access control
- Auditing AI interactions and query logs
Case Studies and Enterprise Use Cases
- AI-powered recommendation systems with Postgres
- Enterprise search and analytics with embeddings
- Automation and predictive modeling within Postgres
Summary and Next Steps
요건
- An understanding of SQL and relational database concepts
- Experience with Postgres administration or development
- Basic familiarity with AI and machine learning principles
Audience
- Database administrators who wish to integrate AI into Postgres
- Data engineers building AI-powered database pipelines
- Developers and architects designing intelligent data-driven applications
21 시간
