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Duration 21 hours
Course Outline
Introduction to AI in Postgres
- Overview of AI and data-driven systems
- Exploring AI use cases within Postgres environments
- Architectural considerations for supporting AI workloads
Setting Up the Environment
- Installing PostgreSQL and configuring the pgvector extension
- Preparing the Python environment for AI integrations
- Connecting Postgres to local and cloud-based LLMs
AI Extensions and Vector Databases
- Understanding the role of vector embeddings in Postgres
- Leveraging pgvector for similarity search and semantic queries
- Comparing AI extensions with external vector stores through benchmarking
Integrating LLMs with Postgres
- Connecting Postgres with OpenAI, Deepseek, Qwen, and Mistral Small
- Designing efficient AI query pipelines
- Implementing strategies for efficient embedding storage and retrieval
Building Intelligent Query Systems
- Translating natural language to SQL using LLMs
- Automating the generation and optimization of queries
- Enhancing database search and summarization with AI assistance
Optimizing Postgres for AI Workloads
- Developing indexing strategies tailored for embeddings
- Tuning performance and implementing caching for AI queries
- Scaling Postgres using distributed and cloud architectures
Security and Governance in AI-Enabled Databases
- Addressing data privacy and compliance requirements
- Managing API keys and enforcing access controls
- Conducting audits of AI interactions and query logs
Case Studies and Enterprise Use Cases
- Implementing AI-powered recommendation systems with Postgres
- Enhancing enterprise search and analytics using embeddings
- Applying automation and predictive modeling within Postgres
Summary and Next Steps
Requirements
- Proficiency in SQL and relational database concepts
- Practical experience with Postgres administration or development
- Fundamental knowledge of AI and machine learning principles
Target Audience
- Database administrators aiming to integrate AI features into Postgres
- Data engineers developing AI-driven database pipelines
- Developers and architects designing intelligent, data-centric applications
