Getting Started - AI Factory 101
================================

**Take your AI Factory skills to the next level** — master complex
Assistants, advanced data pipelines, and enterprise-grade model serving.

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Who Should Take This Path
-------------------------

This intermediate learning path is designed for:

- **Application Developers** integrating AI features into production
  systems

- **Data Engineers** building robust pipelines for AI-driven Knowledge
  Bases

- **AI & Platform Engineers** implementing model serving and
  observability

- **Enterprise Teams** deploying Sovereign AI workloads at scale

**Prerequisites:** Completion of :ref:`Getting Started - AI Factory 101 <Getting Started - AI Factory 101>`  or equivalent experience
with basic AI Factory concepts.

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Learning Outcomes
-----------------

Upon completing this path, you will master:

OK **Advanced AI Orchestration** — Build multi-step Assistants with
complex personas, persistent memory, and tool integrations

OK **Enterprise Data Pipelines** — Design Hybrid Knowledge Bases with
multi-source RAG, metadata filtering, and hybrid search

OK **Production Model Serving** — Deploy and optimize GPU-powered models
using KServe with performance tuning

OK **Operational Excellence** — Implement comprehensive observability,
monitoring, and governance for AI-driven applications

**Estimated Total Time:** 2-3 hours \| **Difficulty:** Intermediate

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Learning Path Modules
=====================

1. Advanced Assistant & Structure Design
----------------------------------------

- Master sophisticated AI orchestration patterns\*

**What You’ll Learn:**

- Design Assistants with nuanced personas and contextual memory

- Build reliable multi-step Structures with error handling

- Integrate external Tools and data sources securely

**Time Investment:** 30-45 minutes

Modules:
^^^^^^^^

1. \*\* :ref:`Assistants Reference Manual <Assistants Reference Manual>`  \*\* — Deep dive into assistant architecture,
   persona design, and memory management

2. \*\* :ref:`Structures Reference Manual <Structures Reference Manual>`  \*\* — Master workflow orchestration with
   branching logic and validation patterns

3. \*\* :ref:`Create an Assistant in Gen AI Builder <Create an Assistant in Gen AI Builder>`  \*\* — Hands-on: Build a production assistant with
   integrated tools and knowledge bases

4. \*\* :ref:`Create a Structure in Gen AI Builder <Create a Structure in Gen AI Builder>`  \*\* — Hands-on: Implement complex workflows with
   conditional logic and error recovery

5. \*\* :ref:`Create a Tool in Gen AI Builder <Create a Tool in Gen AI Builder>`  \*\* — Hands-on: Wrap internal APIs as governed,
   secure AI tools

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2. Data Engineering & Hybrid Knowledge Bases
--------------------------------------------

- Build enterprise-grade data pipelines for AI\*

**What You’ll Learn:**

- Architect Hybrid Knowledge Bases for optimal performance and cost

- Implement advanced RAG patterns with multiple data sources

- Configure intelligent search with metadata filtering and ranking

**Time Investment:** 30-45 minutes

.. _modules-1:

Modules:
^^^^^^^^

1. \*\* :ref:`Best practices for Hybrid Knowledge Bases <Best practices for Hybrid Knowledge Bases>`  \*\* — Proven patterns for balancing data
   freshness, coverage, and operational costs

2. \*\* :ref:`Manage Knowledge Bases in Gen AI Builder <Manage Knowledge Bases in Gen AI Builder>`  \*\* — Operational workflows: re-embedding
   strategies, source lifecycle management, and access controls

3. \*\* :ref:`Create a Retriever in Gen AI Builder <Create a Retriever in Gen AI Builder>`  \*\* — Hands-on: Fine-tune search behavior,
   implement advanced filtering, and optimize ranking algorithms

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3. Model Serving with KServe
----------------------------

- Deploy and scale GPU-powered AI models\*

**What You’ll Learn:**

- Deploy high-performance models on GPU infrastructure

- Optimize resource allocation and runtime configurations

- Manage the complete model deployment lifecycle

**Time Investment:** 45-60 minutes

.. _modules-2:

Modules:
^^^^^^^^

1. \*\* :ref:`Model Serving Concepts <Model Serving Concepts>`  \*\* — Understand KServe architecture and
   deployment patterns

2. \*\* :ref:`How-To Configure a ClusterServingRuntime <How-To Configure a ClusterServingRuntime>`  \*\* — Define optimized runtimes for model
   families with shared configurations

3. \*\* :ref:`How-To Deploy an NVIDIA NIM Microservices available on build.nvidia.comcontainer with KServe <How-To Deploy an NVIDIA NIM Microservices available on build.nvidia.comcontainer with KServe>`  \*\* — Hands-on: Leverage NVIDIA NIM for
   accelerated inference performance

4. \*\* :ref:`How-To Update GPU Resources for an InferenceService <How-To Update GPU Resources for an InferenceService>`  \*\* — Hands-on: Right-size GPU and CPU resources
   for optimal cost-performance

5. \*\* :ref:`How-To Verify InferenceServices and GPU usage <How-To Verify InferenceServices and GPU usage>`  \*\* — Validate endpoints and monitor GPU
   utilization through Hybrid Manager

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4. Observability & Production Monitoring
----------------------------------------

- Ensure reliable, monitored AI operations\*

**What You’ll Learn:**

- Implement comprehensive observability for AI pipelines

- Monitor model performance, resource usage, and system health

- Establish production readiness and SLA compliance

**Time Investment:** 20-30 minutes

.. _modules-3:

Modules:
^^^^^^^^

1. \*\* :ref:`Observability for Model Serving <Observability for Model Serving>`  \*\* — Configure metrics, logging, and alerting
   for models and data pipelines

2. \*\* :ref:`How-To Monitor deployed models with KServe <How-To Monitor deployed models with KServe>`  \*\* — Track endpoint health, latency, and
   throughput in production

3. \*\* :ref:`Observability for Model Serving <Observability for Model Serving>`  \*\* — Leverage platform dashboards and automated
   alerting systems

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Continue Your Learning Journey
==============================

What’s Next?
------------

**Ready for Advanced Patterns?** Progress to \*\* :ref:`Advanced AI Factory Usage and Extensibility - AI Factory 301 <Advanced AI Factory Usage and Extensibility - AI Factory 301>`  \*\* to
master:

- Multi-agent orchestration and complex workflows

- Advanced embedding pipelines and vector optimization

- Enterprise governance and compliance frameworks

- Scaling patterns for high-volume AI applications

Additional Resources
--------------------

**Deepen Your Understanding:**

- :ref:`Gen AI Builder Concepts (Assistants, Knowledge, Tools, Threads) <Gen AI Builder Concepts (Assistants, Knowledge, Tools, Threads)>` 

- `Hybrid Manager: Gen AI Builder Guide <https://www.enterprisedb.com/docs/edb-postgres-ai/latest/hybrid-manager/ai-factory/gen-ai>`_ 

- :ref:`Sovereign AI Implementation Guide <Sovereign AI Explained>` 

- :ref:`Model Serving Concepts <Model Serving Concepts>` 

- :ref:`Structures Reference Manual <Structures Reference Manual>` 

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Ready to Build Enterprise AI?
-----------------------------

Complete this 201 path to gain the expertise needed for deploying
**production-grade Sovereign AI applications** with full control over
your models, data pipelines, and operational infrastructure using **EDB
Postgres AI**.

**Start your first module:** :ref:`Assistants Reference Manual <Assistants Reference Manual>`