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. Assistants — Design assistants with personas, memory, and tool usage patterns.
2. Structures — Learn how to chain retrieval and actions into reliable flows.
3. Create an Assistant — Build a production‑ready assistant with tools and KBs.
4. Create a Structure — Implement multi‑step workflows with validations and branching.
5. Create a Tool — 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. Hybrid Knowledge Base Best Practices — Patterns to balance freshness, coverage, and cost.
2. Manage Knowledge Bases — Operate KBs: re‑embedding, source lifecycle, and access control.
3. Create a Retriever — 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. Model Serving Concepts
2. Configure ServingRuntime — Define runtimes to host families of models with shared settings.
3. Deploy a NIM Container — Use NVIDIA NIM images for high‑performance inference.
4. Update GPU Resources — Right‑size GPU/CPU for performance and cost.
5. Verify Model Deployments — 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. Observability for Model Serving — Metrics and logs for models and pipelines.
2. Monitor InferenceService — Track health and performance of serving endpoints.
3. Hybrid Manager Observability — Platform‑level dashboards and alerts.

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

• AI Factory Concepts
• Hybrid Manager: Using Gen AI Builder
• Sovereign AI Explained
• Model Serving Concepts
• Structures

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.