AI Factory 201 Path — Building Production-Ready AI Features v1.2

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Who this is for

  • Developers integrating Gen AI features into production applications
  • Data teams building pipelines for AI-driven Knowledge Bases
  • MLOps 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. Assistants Explained
  2. Structures Explained
  3. Create an Assistant
  4. Create a Structure
  5. Create a Tool

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. Hybrid Knowledge Base Best Practices
  2. Manage Knowledge Bases
  3. Create a Retriever

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. Model Serving Concepts
  2. Configure ServingRuntime
  3. Deploy a NIM Container
  4. Update GPU Resources
  5. Verify Model Deployments

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. Observability for Model Serving
  2. Monitor InferenceService
  3. Hybrid Manager Observability

Next steps

After completing this 201 Path:

  • Continue to AI Factory 301 Path — advanced patterns for scaling AI apps, multi-agent orchestration, embedding pipelines, and advanced governance.

Related learning resources

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.

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AI Factory 301 Path — Advanced AI Factory Usage and Extensibility