Learning Paths

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, ML 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?

• AI Factory Concepts
• How-To Guides
• Model Serving Quickstart