AI Solution Architecture

Book

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

Courses:

AI Architecture Course Topics:

(https://www.umbctraining.com/courses/ai-for-architects)

Upon completing this course, participants will be able to:

Understand the full AI solution lifecycle, from data acquisition and training to deployment and ongoing operations Evaluate architectural trade-offs across model training, inference, scale, and cost Design systems that incorporate AI agents, multi-agent collaboration, and orchestration frameworks Apply DevOps, CI/CD, and GitOps principles to AI workloads, ensuring repeatable and scalable delivery Integrate observability, governance, and monitoring practices to manage drift, data quality, and performance Architect secure and responsible AI systems that address privacy, compliance, and ethical considerations Anticipate Day 2 challenges, including model lifecycle management, versioning, and integration with enterprise platforms

Course Outline

Day 1: AI Software & Agentic Architectures

AI Architecture Overview – Patterns, reference models, and integration with enterprise systems Training & Inference – Data pipelines, model development, serving, and scaling Agent Architectures – Foundations of AI agents, tools, and context management Multi-Agent Collaboration – Orchestration, coordination, and emerging standards

Day 2: Platforms, CI/CD, and Orchestration

AI Platform Tooling – Model registries, orchestration engines, and agent frameworks AI CI/CD – Automating training, testing, deployment, and rollback GitOps for AI – Declarative operations for ML and agent-based systems Observability & Monitoring – Tracking performance, drift detection, and behavior of agents

Day 3: AI Day 2 Operations & Governance

AI Security – Protecting data, models, and multi-agent systems from threats AI Privacy & Responsible AI – Fairness, bias, compliance, and ethical design Lifecycle Management – Versioning, retraining, and managing evolving agent behaviors Governance & Strategy – Organizational adoption, cost management, and long-term sustainability

The Agent Technical Course: Build and Deploy Production-Grade Gen AI Products

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

  1. Agentic RAG with Routers — Why Naive RAG Breaks

We begin by deconstructing naive RAG — systems that fail under multi-turn, context-rich queries. You’ll build your own agentic retrieval system with intelligent routers, reflection, memory, and reasoning — capable of tool invocation, multi-agent coordination, and smart chunk selection.

🔍 You’ll learn:

-Stateless vs. stateful RAG

-When cosine similarity fails

-Designing context-aware routing logic

-Reflection, ReAsk, and multi-hop search strategies

  1. Hosting & Quantizing LLMs — Local + Runpod

Production-ready agents can’t always rely on OpenAI.

You’ll learn to quantize models (GPTQ, GGUF) for speed and cost-efficiency, and deploy them with Ollama locally and RunPod in the cloud, with tools like FastAPI and auto-scaling on demand.

🚀 You’ll learn:

-LLM quantization strategies (4-bit, GGML, QLoRA)

-On-device hosting via Ollama

-Deployment via RunPod or serverless GCP

-Streamed inference + latency benchmarking

  1. Semantic Caching — Build It from Scratch

We’ll implement a semantic caching layer from scratch that recognizes similar queries, avoids unnecessary calls to the model, and improves performance over time using vector proximity and feedback loops.

💡 You’ll build:

Feedback loop to train your cache

Cache hit/miss architecture

Semantic distance functions + reranking

Cost-saving and latency benchmarks

  1. Knowledge Graphs from Scratch — Text-to-Cypher

Go beyond flat retrieval with structured reasoning using Knowledge Graphs. You’ll implement a graph-based memory layer with Cypher generation from natural language, and use DSPy to guide model outputs toward your schema.

🌐 You’ll learn:

Graph modeling for agent memory

Extracting entities + relations from unstructured text

Generating Cypher queries from prompts

Integrating Neo4j or Memgraph with RAG

  1. ReAct Agents — Python & No-Code with n8n

We’ll go deep into ReAct (Reason + Act) — one of the most powerful agent paradigms — and then rebuild it using both Python and no-code tools like n8n. Perfect for teams and workflows where technical + non-technical builders collaborate.

🔧 You’ll build:

Modular ReAct pipelines (tool use, planning, reflection)

Human-in-the-loop agents

No-code agents in n8n connected to APIs + databases

Multi-step workflows with visual orchestration

  1. Bringing It All Together — ADK, MCP, A2A, and Guardrails

The final sprint: we combine everything into a production system using Google’s ADK (Agent Development Kit), implement MCP (Modular Cognitive Planning), and create agent-to-agent (A2A) collaboration. You’ll also implement industrial-grade guardrails and deploy securely with GCP integrations.

🛡️ You’ll ship:

Multi-agent collaboration workflows

-Safety guardrails using Llama Guard