Learning Paths v1.3.1

The November 2025 Innovation Release of EDB Postgres AI is available. For more information, see the release notes.

AI Factory Learning Paths provide structured guidance to help you build AI-powered features and intelligent applications using AI Factory — with full control and governance.

Whether you're just starting or scaling Sovereign AI solutions in production, these paths guide you through key concepts, tools, and practical implementation — building on your experience as you progress.

How to use the Learning Paths

Each path is self-paced and modular.

You can:

  • Complete paths sequentially (101 → 201 → 301), or
  • Dive into topics based on your role and project needs.

For each path:

  • Review the prerequisites to ensure readiness.
  • Follow the learning flow of:
  • Concepts → How-To Guides → Tutorials → Practice
  • Use your existing AI Factory environment or a sandbox project to practice hands-on.

Learning Paths

101 Path — Getting Started with AI Factory

Audience: New users, developers, data engineers, architects. Estimated time: 1–2 hours. Prerequisites: Familiarity with basic AI concepts and using web applications.

You will learn to:

  • Understand core AI Factory concepts
  • Navigate AI Factory and Hybrid Manager
  • Create your first AI Assistants
  • Connect Knowledge Bases and Retrievers
  • Run and review interaction Threads
  • Understand key AI Factory terminology

Start the 101 Path →

201 Path — Building Production-Ready AI Features

Audience: Developers, data engineers, solution architects building production AI features. Estimated time: 2–4 hours. Prerequisites: Completion of 101 Path, basic knowledge of Kubernetes concepts.

You will learn to:

  • Architect hybrid Knowledge Bases for advanced search
  • Use and manage Rulesets and governance patterns
  • Build advanced Assistants and multi-source Retrievers
  • Implement GPU-powered Model Serving with KServe
  • Apply monitoring and observability best practices
  • Implement production-readiness and scaling strategies

Start the 201 Path →

301 Path — Advanced AI Factory Usage and Extensibility

Audience: AI platform owners, advanced developers, AI engineers, architects. Estimated time: 4–6 hours (self-paced, advanced topics). Prerequisites: Completion of 201 Path, experience with Kubernetes and container-based AI workloads.

You will learn to:

  • Design Agentic Assistants and advanced Structures
  • Develop and deploy custom Tools
  • Extend Model Serving with custom ServingRuntimes
  • Implement model explainability and responsible AI patterns
  • Automate AI Factory pipelines via API-driven workflows
  • Apply advanced observability and performance tuning

Start the 301 Path →

Recommended Training Courses

To complement these self-paced Learning Paths, we also offer:

Instructor-Led Training

  • Advanced AI Factory Architectures
  • AI Factory Administration & Operations
  • Custom Model Development & Deployment with AI Factory

Self-Paced Training

  • Introduction to AI Factory
  • Building AI Assistants with AI Factory
  • Managing AI Models and Hybrid Knowledge Bases
  • Scaling AI Workloads with AI Factory

Where to next?