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-1: Learning Paths ============== ``---`` :ref:`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 :ref:`Getting Started - AI Factory 101 ` ``---`` :ref:`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 :ref:`Building Production-Ready AI Features - AI Factory 201 ` ``---`` :ref:`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 :ref:`Advanced AI Factory Usage and Extensibility - AI Factory 301 ` ``---`` 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? ============== - :ref:`Gen AI Builder Concepts (Assistants, Knowledge, Tools, Threads) ` - :ref:`Gen AI How-To Guides ` - :ref:`Model Serving Quickstart ` ``---``