Advanced AI Factory Usage and Extensibility - AI Factory 301

Advanced AI Factory Usage and Extensibility - AI Factory 301#

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

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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

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Modules by Focus Area#

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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 Reference Manual — Architect assistants that reason, retrieve, and act across tools.

  2. Tools Reference Manual — Design governed actions that call internal or external systems.

  3. Create a Tool in Gen AI Builder — Implement robust, reusable tool integrations for agent workflows.

  4. Structures Reference Manual — Build multi-step pipelines and agent behaviors.

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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. How-To Configure a ClusterServingRuntime — Create custom runtimes for specialized models and settings.

  3. How-To Deploy AI Models from Model Library (placeholder — expand with custom Transformers/Explainers patterns)

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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. How-To Monitor deployed models with KServe — Health and performance of serving endpoints.

  3. Observability for Model Serving

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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

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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.

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