Building Production-Ready AI Features - AI Factory 201 ====================================================== Who this is for --------------- - Developers integrating Gen AI features into production applications - Data teams building pipelines for AI-driven Knowledge Bases - AI and platform teams configuring model serving and observability - Hybrid Manager users enabling Sovereign AI workloads at scale ``---`` General Goals ------------- By completing this path, you will: - Build **multi-step AI Assistants** and advanced Structures - Design **Hybrid Knowledge Bases** and multi-source RAG pipelines - Deploy and manage **GPU-powered Model Serving** - Implement **observability and monitoring** for AI-driven features - Learn patterns for production-grade governance and performance tuning ``---`` Modules by Focus Area ===================== ``---`` 1. Advanced Assistant & Structure Design ---------------------------------------- **Goals:** - Implement Assistants with complex personas and memory - Create advanced multi-step Structures - Integrate Tools and external data flows **Estimated Time:** ~30–45 min **Modules:** 1. :ref:`Assistants Reference Manual ` — Design assistants with personas, memory, and tool usage patterns. 2. :ref:`Structures Reference Manual ` — Learn how to chain retrieval and actions into reliable flows. 3. :ref:`Create an Assistant in Gen AI Builder ` — Build a production‑ready assistant with tools and KBs. 4. :ref:`Create a Structure in Gen AI Builder ` — Implement multi‑step workflows with validations and branching. 5. :ref:`Create a Tool in Gen AI Builder ` — Wrap internal services and APIs as safe, governed tools. ``---`` 2. Data Engineering & Hybrid Knowledge Bases -------------------------------------------- **Goals:** - Design and manage Hybrid Knowledge Bases - Tune multi-source RAG pipelines - Implement metadata filtering and hybrid search **Estimated Time:** ~30–45 min **Modules:** 1. :ref:`Best practices for Hybrid Knowledge Bases ` — Patterns to balance freshness, coverage, and cost. 2. :ref:`Manage Knowledge Bases in Gen AI Builder ` — Operate KBs: re‑embedding, source lifecycle, and access control. 3. :ref:`Create a Retriever in Gen AI Builder ` — Tune search behavior, filters, and ranking at scale. ``---`` 3. Model Serving with KServe ---------------------------- **Goals:** - Deploy GPU-powered models - Tune runtime and resource settings - Understand the Model Serving lifecycle **Estimated Time:** ~45–60 min **Modules:** 1. :ref:`Model Serving Concepts ` 2. :ref:`How-To Configure a ClusterServingRuntime ` — Define runtimes to host families of models with shared settings. 3. :ref:`How-To Deploy an NVIDIA NIM Microservices available on build.nvidia.comcontainer with KServe ` — Use NVIDIA NIM images for high‑performance inference. 4. :ref:`How-To Update GPU Resources for an InferenceService ` — Right‑size GPU/CPU for performance and cost. 5. :ref:`How-To Verify InferenceServices and GPU usage ` — Validate endpoints and GPU usage in Hybrid Manager. ``---`` 4. Observability & Monitoring ----------------------------- **Goals:** - Implement observability for AI pipelines and Model Serving - Monitor performance and resource usage - Enable production readiness checks **Estimated Time:** ~20–30 min **Modules:** 1. :ref:`Observability for Model Serving ` — Metrics and logs for models and pipelines. 2. :ref:`How-To Monitor deployed models with KServe ` — Track health and performance of serving endpoints. 3. :ref:`Observability for Model Serving ` — Platform‑level dashboards and alerts. ``---`` Next steps ========== After completing this 201 Path: - Continue to :ref:`Advanced AI Factory Usage and Extensibility - AI Factory 301 ` — advanced patterns for scaling AI apps, multi-agent orchestration, embedding pipelines, and advanced governance. ``---`` Related learning resources ========================== - :ref:`Gen AI Builder Concepts (Assistants, Knowledge, Tools, Threads) ` - `Hybrid Manager: Using Gen AI Builder `_ - :ref:`Sovereign AI Explained ` - :ref:`Model Serving Concepts ` - :ref:`Structures Reference Manual ` ``---`` By mastering this 201 Path, you’ll be ready to deploy and scale **Sovereign AI** applications — with full control over your models, pipelines, and production observability — using **EDB PG AI**. ``---``