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.
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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.
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Learning Paths#
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Getting Started - AI Factory 101#
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
Getting Started - AI Factory 101
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Building Production-Ready AI Features - AI Factory 201#
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
Building Production-Ready AI Features - AI Factory 201
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Advanced AI Factory Usage and Extensibility - AI Factory 301#
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
Advanced AI Factory Usage and Extensibility - AI Factory 301
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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
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Where to next?#
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