AI Solution Architecture Learning Path

Goal

Build production AI systems with proper architecture, agent orchestration, and operational practices.

Resources

Book

https://docs.google.com/document/d/1rsaK53T3Lg5KoGwvf8ukOUvbELRtH-V0LnOIFDxBryE/edit?tab=t.0

Architecture Courses

AdaSci - Agentic AI System Architect
https://adasci.org/courses/adasci-certified-agentic-ai-system-architect/

UMBC - AI for Architects
https://www.umbctraining.com/courses/ai-for-architects

AI Architecture Fundamentals

Day 1: Core Architecture

• AI patterns and enterprise integration
• Training & inference pipelines
• Agent architectures: tools and context management
• Multi-agent collaboration and orchestration

Day 2: Platform Operations

• Model registries and orchestration engines
• CI/CD automation for training and deployment
• GitOps for ML systems
• Observability: drift detection and agent behavior tracking

Day 3: Production Operations

• Security for data, models, and multi-agent systems
• Privacy, fairness, and compliance
• Model versioning and lifecycle management
• Governance and cost management

Advanced: Production Gen AI

https://maven.com/boring-bot/advanced-llm

1. Agentic RAG with Routers

Build context-aware retrieval with routers, reflection, and memory.

Topics:

• Stateless vs. stateful RAG
• Context-aware routing logic
• Reflection, ReAsk, multi-hop search
• Tool invocation and multi-agent coordination

2. Hosting & Quantizing LLMs

Deploy models locally and in cloud with quantization.

Stack:

• Quantization: GPTQ, GGUF, 4-bit, GGML, QLoRA
• Local hosting: Ollama
• Cloud deployment: RunPod, GCP serverless
• API layer: FastAPI with streamed inference

3. Semantic Caching

Reduce costs and latency by caching similar queries using vector proximity.

Implementation:

• Cache hit/miss architecture
• Semantic distance functions + reranking
• Feedback loop for cache training
• Cost and latency benchmarking

4. Knowledge Graphs

Structured reasoning with graph-based memory.

Stack:

• Graph databases: Neo4j, Memgraph
• Text-to-Cypher generation with DSPy
• Entity and relation extraction
• Graph-integrated RAG

5. ReAct Agents

Build Reason + Act agents in code and no-code.

Implementations:

• Python: modular pipelines with tool use, planning, reflection
• No-code: n8n with API and database connections
• Human-in-the-loop workflows
• Visual orchestration for multi-step processes

6. Production System Integration

Combine everything into a secure, monitored production system.

Stack:

• Google ADK (Agent Development Kit)
• MCP (Modular Cognitive Planning)
• Agent-to-agent (A2A) collaboration
• Safety guardrails: Llama Guard
• GCP deployment with monitoring