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Course Outline

Introduction to AI in Postgres

  • Overview of AI and data-driven systems.
  • Key AI use cases within Postgres environments.
  • Architectural considerations for supporting AI workloads.

Setting Up the Environment

  • Installing PostgreSQL and configuring pgvector.
  • Preparing Python environments for AI integrations.
  • Connecting Postgres to both local and cloud-based LLMs.

AI Extensions and Vector Databases

  • Understanding vector embeddings within Postgres.
  • Utilizing pgvector for similarity search and semantic queries.
  • Benchmarking AI extensions against external vector stores.

Integrating LLMs with Postgres

  • Connecting Postgres with OpenAI, Deepseek, Qwen, and Mistral Small.
  • Designing efficient AI query pipelines.
  • Efficiently storing and retrieving embeddings.

Building Intelligent Query Systems

  • Converting natural language to SQL using LLMs.
  • Automating query generation and optimization processes.
  • Implementing AI-assisted database search and summarization.

Optimizing Postgres for AI Workloads

  • Advanced indexing strategies for embeddings.
  • Performance tuning and caching mechanisms 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 access controls effectively.
  • Auditing AI interactions and monitoring query logs.

Case Studies and Enterprise Use Cases

  • Building AI-powered recommendation systems with Postgres.
  • Enhancing enterprise search and analytics with embeddings.
  • Leveraging automation and predictive modeling within Postgres.

Summary and Next Steps

Requirements

  • A solid grasp of SQL and relational database concepts.
  • Practical experience in Postgres administration or development.
  • Basic familiarity with core principles of AI and machine learning.

Target Audience

  • Database administrators looking to integrate AI capabilities into Postgres.
  • Data engineers constructing AI-powered database pipelines.
  • Developers and architects designing intelligent, data-driven applications.
 21 Hours

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Provisional Upcoming Courses (Require 5+ participants)

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