Architect Overview

20 labs · Docker-verified (zchencow/innozverse-ai:latest)

Enterprise AI platform design, MLOps, LLM infrastructure, AI safety, governance, and security.

---

Labs

#	Lab	Topic
01	MLOps Platform Architecture	MLflow, CI/CD for ML
02	Model Serving at Scale	FastAPI serving, canary
03	Vector Database Architecture	pgvector, HNSW, ANN
04	LLM Infrastructure Design	GPU, quantization, KV cache
05	RAG at Scale	chunking, hybrid search, re-ranking
06	AI Observability & Monitoring	drift, KS test, alerting
07	Federated Learning at Scale	FedAvg, differential privacy
08	Multi-Agent System Design	ReAct, orchestration, memory
09	AI Security Red Team	prompt injection, MITRE ATLAS
10	EU AI Act Compliance	risk tiers, GPAI, NIST AI RMF
11	AI Cost Optimization	GPU utilization, distillation, ROI
12	Enterprise AI Platform	self-service ML, governance
13	AI Data Pipeline Architecture	feature store, DVC, lineage
14	Responsible AI Audit	fairness, SHAP, model cards
15	LLM Fine-Tuning Infrastructure	LoRA, QLoRA, DPO, RLHF
16	Knowledge Graph + LLM	GraphRAG, Neo4j, SPARQL
17	Real-Time AI Inference	streaming, latency budget
18	AI-Driven SOC Automation	UEBA, SIEM ML, ATT&CK
19	Distributed Training Architecture	DDP, ZeRO, gradient compression
20	Capstone — Enterprise AI Security Platform	full platform design

---

Start here → Lab 01: MLOps Platform Architecture