Who this is for

• Advanced developers building AI-first applications and agents
• AI OPs teams managing large-scale model serving
• Platform engineers building extensions and integrations with AI Factory
• Hybrid Manager users building enterprise-grade Sovereign AI solutions

General Goals

By completing this path, you will:

• Build complex agentic Assistants with Tool calling and advanced RAG pipelines
• Extend Model Serving with custom runtimes, Transformers, and Explainers
• Automate and orchestrate AI Factory pipelines with APIs
• Design advanced observability and governance for production AI
• Integrate AI Factory with enterprise systems and existing AI pipelines

Modules by Focus Area

1. Agentic Assistants and Advanced Tools

Goals:

• Build Assistants that reason and act with multi-step Tool usage
• Implement advanced Tools for external API integration
• Chain Tools and manage context flow

Estimated Time: ~45–60 min

Modules:

1. Assistants — Architect assistants that reason, retrieve, and act across tools.
2. Tools — Design governed actions that call internal or external systems.
3. Create a Tool — Implement robust, reusable tool integrations for agent workflows.
4. Structures — Build multi-step pipelines and agent behaviors.

2. Extending Model Serving

Goals:

• Create custom ServingRuntime definitions for specialized models
• Add Transformers and Explainers to Model Serving pipelines
• Understand advanced deployment patterns for KServe-based serving

Estimated Time: ~45–60 min

Modules:

1. Model Serving Concepts
2. Configure ServingRuntime — Create custom runtimes for specialized models and settings.
3. Advanced ServingRuntime Configuration (placeholder — expand with custom Transformers/Explainers patterns)

3. Observability and Governance at Scale

Goals:

• Implement enterprise-grade observability for AI pipelines and Model Serving
• Monitor Tool usage and pipeline performance
• Build custom dashboards for hybrid AI workloads

Estimated Time: ~30–45 min

Modules:

1. Observability for Model Serving — Metrics, logs, and traces for serving and pipelines.
2. Monitor InferenceService — Health and performance of serving endpoints.
3. Hybrid Manager Observability

4. Automation and API Integration

Goals:

• Use Hybrid Manager and AI Factory APIs to automate workflows
• Integrate AI Factory pipelines into CI/CD and AI systems
• Manage large-scale AI Factory deployments via API-driven control

Estimated Time: ~20–30 min

Modules:

1. Hybrid Manager API Overview
2. Using the Hybrid Manager API

Next steps

After completing this 301 Path:

• You will be equipped to design Sovereign AI applications at enterprise scale.
• You will understand advanced integration patterns with AI Factory, Hybrid Manager, and existing AI pipelines.
• You will be ready to contribute advanced workflows and reusable components (Tools, Structures, Transformers) to your AI Factory ecosystem.

Related learning resources

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

By mastering this 301 Path, you will be ready to drive advanced AI innovation across your organization — fully under your control — using EDB PG AI and Hybrid Manager.