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

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 AI Factory 101 or equivalent experience with basic AI Factory concepts.

Learning Outcomes

Upon completing this path, you will master:

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

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

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

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

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

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. Assistants — Deep dive into assistant architecture, persona design, and memory management
2. Structures — Master workflow orchestration with branching logic and validation patterns
3. Create an Assistant — Hands-on: Build a production assistant with integrated tools and knowledge bases
4. Create a Structure — Hands-on: Implement complex workflows with conditional logic and error recovery
5. Create a Tool — Hands-on: Wrap internal APIs as governed, secure AI tools

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. Hybrid Knowledge Base Best Practices — Proven patterns for balancing data freshness, coverage, and operational costs
2. Manage Knowledge Bases — Operational workflows: re-embedding strategies, source lifecycle management, and access controls
3. Create a Retriever — Hands-on: Fine-tune search behavior, implement advanced filtering, and optimize ranking algorithms

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:

1. Model Serving Concepts — Understand KServe architecture and deployment patterns
2. Configure ServingRuntime — Define optimized runtimes for model families with shared configurations
3. Deploy a NIM Container — Hands-on: Leverage NVIDIA NIM for accelerated inference performance
4. Update GPU Resources — Hands-on: Right-size GPU and CPU resources for optimal cost-performance
5. Verify Model Deployments — Validate endpoints and monitor GPU utilization through Hybrid Manager

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:

1. Observability for Model Serving — Configure metrics, logging, and alerting for models and data pipelines
2. Monitor InferenceService — Track endpoint health, latency, and throughput in production
3. Hybrid Manager Observability — Leverage platform dashboards and automated alerting systems

Continue Your Learning Journey

What's Next?

Ready for Advanced Patterns? Progress to AI Factory 301 Path 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:

• AI Factory Architecture Overview
• Hybrid Manager: Gen AI Builder Guide
• Sovereign AI Implementation Guide
• Model Serving Deep Dive
• Advanced Structure Patterns

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: Assistants →