🧠 Roadmap AI Engineering Stack — Bare-Metal On-Prem & Homelab
Filosofi
Jangan hanya “fine-tune di Colab” — kamu harus bisa “operate.” Bedanya besar: menjalankan
ollama run llama3itu 2 menit, tapi operate multi-model gateway dengan KV cache optimization, RAG reranking, hallucination tracking, dan automated red-team pipeline itu skill yang membedakan engineer dari notebook hobbyist. Rekruter akan tanya: “Latency melonjak 400ms saat context window penuh, apa yang terjadi di backend dan bagaimana kamu mitigasi tanpa ganti hardware?” Kalau kamu jawab: “KV cache eviction policy diaktifkan, speculative decoding di-disable untuk prioritas throughput, fallback ke smaller quantized model via LiteLLM router, dan Langfuse trace menunjukkan bottleneck di embedding retrieval, bukan LLM inference” — itu yang menutup pertanyaan.
🎯 Checkpoint Awal — Sebelum Mulai
- Stack: Ollama / vLLM (single-node) → FastAPI → PostgreSQL + pgvector / Qdrant
- Jalur: AI Engineer → MLOps/LLMOps Engineer → AI Infrastructure Lead
- Spek Dasar: i7 Gen7, 8GB RAM, GTX 1050 (⚠️ VRAM 2GB sangat terbatas. Kuantisasi 4-bit/8-bit, CPU offload, atau cloud GPU spot instance wajib untuk fase 3+)
- Target Karir: AI Systems Engineer → AI Platform Lead → MLOps Architect
Homelab Strategy untuk VRAM/RAM Terbatas
- Fase 1-2: CPU inference + Q4_K_M quantization, model ≤ 3B, Docker resource limits ketat.
- Fase 3-4: RAG pipeline jalankan terpisah dari inference server, gunakan SQLite/Chroma dulu sebelum Qdrant untuk hemat RAM.
- Fase 5-6: Cloud GPU trial (RunPod / Vast.ai ~$0.20/jam) hanya untuk evaluation benchmark & red-team stress test.
- Prioritas: Data pipeline reproducibility → Serving latency control → Evaluation metrics → Security guardrails.
Urutan Belajar
- Fase 1 (Foundation): Local serving + quantization + OpenAI-compatible API
- Fase 2 (Data): Ingestion pipeline + chunking strategy + vector DB + embedding selection
- Fase 3 (Architecture): Advanced RAG + reranking + agent state machine + caching
- Fase 4 (MLOps): Experiment tracking + DVC + CI/CD for AI + versioned prompts
- Fase 5 (Observe & Secure): Tracing + evaluation framework + guardrails + automated red team
- Fase 6 (Operate & Scale): Multi-model routing + inference optimization + AI compliance + cost control
Next step: Install Ollama/vLLM, pull Qwen2.5-1.5B-Q4_K_M, expose OpenAI-compatible API via FastAPI, ukur tokens/sec dan TTFT (Time To First Token).
Aturan Emas
Satu pipeline dulu. Selesaikan sampai bisa reproduce experiment, track version, monitor latency, dan block malicious prompt secara otomatis, baru loncat ke layer berikutnya. AI infrastructure adalah rantai probabilistik — jika satu link lemah (data, cache, guardrail), seluruh output jadi tidak trustworthy.
Fase 1 — Local Model Serving & Quantization (Minggu 1–4)
Goal: Kuasai inference engine, quantization trade-off, dan API standardization. Resource Impact: vLLM/Ollama ~2GB RAM + VRAM tergantung model. Q4_K_M 3B model ≈ 2GB VRAM aman.
| Tool/Stack | Resource | Yang Dipelajari | Combo A+B yang Membuktikan |
|---|---|---|---|
| Ollama / vLLM | Host ~1–2GB RAM + VRAM | PagedAttention, KV cache management, quantization formats (GGUF/AWQ), continuous batching, OpenAI API compatibility | vLLM + benchmarking script = kamu bisa ukur throughput (tokens/sec) vs context length secara empiris |
| FastAPI | ~256MB | Async routing, request validation (Pydantic), streaming response (SSE), health check, rate limiting middleware | FastAPI + vLLM backend = kamu punya production-ready inference endpoint dalam <50 line kode |
| Docker / Compose | ~512MB | Multi-stage build, resource limits (CPU/RAM/VRAM passthrough), network isolation, volume management untuk model weights | Docker + reproducible inference = setup berjalan identik di laptop, server, atau cloud tanpa dependency hell |
| PyTorch Basics | ~1GB | Tensor shape, device placement, model loading, torch.no_grad(), memory profiling (torch.cuda.memory_summary) | PyTorch + memory audit = kamu paham kenapa OOM terjadi dan cara debug tanpa tebak-tebakan |
Cara Belajar Fase 1
Deploy vLLM di bare metal. Expose OpenAI-compatible API. Kirim 100 concurrent request. Ukur TTFT (Time To First Token), P99 latency, dan GPU utilization. Ganti quantization Q4 → Q8 → FP16. Dokumentasikan trade-off. Ulangi sampai pipeline tidak crash saat context window penuh.
Proyek Portofolio Fase 1: Bare-Metal LLM Inference Server — dokumentasi lengkap: vLLM config, quantization benchmark table, Docker Compose file, FastAPI streaming endpoint, load test result (k6/Locust) dengan grafik latency vs concurrency.
Fase 2 — Data Pipeline & Vector Infrastructure (Minggu 5–8)
Goal: Bangun ingestion pipeline yang reproducible, kuasai chunking strategy, dan implement vector search. Resource Impact: Embedding model ~1GB VRAM, Vector DB ~512MB–1GB RAM, ETL tools ~256MB.
| Tool/Stack | Resource | Yang Dipelajari | Combo A+B yang Membuktikan |
|---|---|---|---|
| MinIO / Local Storage | ~256MB | S3-compatible API, versioning, lifecycle policy, TLS for data at rest | MinIO + structured dataset version = raw data tidak pernah hilang atau teroverwrite tanpa audit trail |
| pgvector / Qdrant | ~512MB | HNSW index, cosine vs dot product, metadata filtering, ANN vs exact search trade-off | pgvector + recall@K metric = kamu bisa justify kenapa index tertentu dipilih berdasarkan query pattern |
| LangChain / LlamaIndex Core | ~256MB | Document loaders, text splitters (recursive, semantic, markdown), embedding pipeline, vector store integration | LlamaIndex + chunking benchmark = kamu bisa tunjukkan recall improvement dari 300-token fixed vs semantic chunking |
| Unstructured / DuckDB | ~512MB | OCR pipeline, table extraction, schema validation, SQL-on-JSON/Parquet | Unstructured + DuckDB ETL = kamu bisa transform PDF/HTML menjadi structured records tanpa regex hack |
Realita Chunking
Ukuran chunk bukan parameter ajaib. Strategi chunking menentukan recall lebih besar daripada model size. Chunk terlalu kecil = context hilang. Chunk terlalu besar = noise masuk. Selalu ukur recall@K dan precision sebelum deploy.
Proyek Portofolio Fase 2: Document Ingestion & Vector Search Pipeline — diagram data flow, chunking strategy comparison table, embedding model benchmark (speed vs quality), pgvector index config, search API dengan recall/precision metrics.
Fase 3 — Advanced RAG & Agent Architecture (Minggu 9–12)
Goal: Implement hybrid search, reranking, query rewriting, dan agent loop dengan state machine yang deterministic. Resource Impact: Reranker model ~1GB VRAM, Cache/Agent ~512MB RAM, Redis ~256MB.
| Tool/Stack | Resource | Yang Dipelajari | Combo A+B yang Membuktikan |
|---|---|---|---|
| Advanced RAG Patterns | ~256MB | Hybrid search (BM25 + vector), query expansion, self-query, context compression, reranking (Cross-Encoder) | Reranker + precision@3 metric = kamu bisa tunjukkan peningkatan relevansi tanpa tambah context window |
| LangGraph / Custom State Machine | ~512MB | Finite state machine for agents, tool routing, human-in-the-loop, checkpoint/rollback, deterministic vs stochastic flow | LangGraph + tool failure recovery = agent tidak hang atau loop tak terbatas saat tool error |
| Redis / Memcached | ~256MB | Semantic cache, TTL management, cache invalidation strategy, hit-rate monitoring | Redis + semantic cache = kamu reduce API call 40% tanpa degrade response quality |
| Nginx / HAProxy | ~128MB | API routing, request buffering, timeout management, circuit breaker pattern | HAProxy + fallback routing = traffic otomatis switch ke smaller model saat primary latency > threshold |
Agent Tanpa State Machine = Chaos
Framework agent yang mengandalkan “LLM memutuskan sendiri kapan stop” tidak production-ready. Selalu gunakan explicit state graph, define retry limits, dan instrument setiap transition. Determinism > autonomy di production.
Proyek Portofolio Fase 3: Production RAG Agent — architecture diagram (retriever → reranker → generator → tool executor), cache hit-rate dashboard, state machine config, latency breakdown per component, failure recovery log.
Fase 4 — MLOps, Experiment Tracking & CI/CD for AI (Minggu 13–16)
Goal: Version control data, model, dan prompt. Otomasi evaluasi dan deployment pipeline. Resource Impact: MLflow/DVC ~512MB, CI Runner ~1GB, Monitoring ~256MB.
| Tool/Stack | Resource | Yang Dipelajari | Combo A+B yang Membuktikan |
|---|---|---|---|
| MLflow / Weights & Biases | ~512MB | Experiment tracking, model registry, artifact versioning, parameter logging, comparison UI | MLflow + reproducible run = kamu bisa rollback ke experiment spesifik dalam 1 command |
| DVC (Data Version Control) | ~256MB | Data pipeline tracking, remote storage sync, pipeline DAG, metric logging | DVC + Git = data dan kode selalu sinkron, tidak ada “model ini pakai data yang mana?” |
| GitHub Actions | N/A | AI-specific CI: lint prompts, run evaluation suite, build Docker, push to registry, deploy to staging | GitHub Actions + automated eval = model tidak deploy jika hallucination rate > 5% |
| Evidently AI / Prometheus | ~512MB | Data drift detection, concept drift, feature distribution shift, alerting integration | Evidently + drift alert = kamu detect degradation sebelum user komplain |
Prompt Adalah Kode
Jika prompt tidak di-version control, di-review, dan di-test, itu technical debt yang menunggu waktu. Treat prompt seperti production config. Simpan di Git, review via PR, test via automated eval.
Proyek Portofolio Fase 4: AI Model & Prompt CI/CD Pipeline — repo structure, DVC pipeline YAML, MLflow experiment dashboard screenshot, GitHub Actions workflow (eval → gate → deploy), prompt versioning history, rollback procedure.
Fase 5 — Observability, Evaluation & Security (Minggu 17–20)
Goal: Ukur hallucination, latency, cost, dan implement guardrail + automated red team. Resource Impact: Langfuse/Otel ~512MB, Garak/PyRIT ~1GB, Guardrails ~512MB.
| Tool/Stack | Resource | Yang Dipelajari | Combo A+B yang Membuktikan |
|---|---|---|---|
| Langfuse / OpenLLMetry | ~512MB | Distributed tracing, span annotation, cost tracking, user feedback loop, session replay | Langfuse + trace correlation = kamu bisa debug hallucination dari raw prompt sampai final token |
| DeepEval / Ragas | ~256MB | Automated evaluation metrics: faithfulness, answer relevancy, context precision, hallucination score | Ragas + CI gate = deploy diblokir otomatis jika faithfulness < 85% |
| Garak / PyRIT / LlamaGuard | ~1GB | Automated red teaming, prompt injection testing, safety classification, guardrail integration | Garak + LlamaGuard = kamu punya pre-deploy security audit report yang terdokumentasi |
| NeMo Guardrails | ~512MB | Input/output flow definition, regex + LLM-based filters, dynamic policy update, fallback routing | NeMo + Colang flows = prompt injection ter-block tanpa matiin seluruh endpoint |
Evaluasi Tanpa Baseline = Teater
Jangan percaya angka “accuracy 95%” tanpa tahu baseline, dataset composition, dan edge cases. Selalu bandingkan dengan deterministic baseline (keyword search, rule-based system) dan dokumentasikan failure mode.
Proyek Portofolio Fase 5: AI Observability & Security Dashboard — Langfuse trace example, Ragas evaluation report, Garak scan output, NeMo guardrail config, hallucination trend chart, incident response playbook untuk AI failure.
Fase 6 — Advanced Optimization, Scale & Compliance (Minggu 21–24)
Goal: Model routing, inference optimization, AI governance, dan cost-aware operation. Resource Impact: Router/Proxy ~256MB, Optimization tools ~512MB, Compliance docs ~N/A.
| Tool/Stack | Resource | Yang Dipelajari | Combo A+B yang Membuktikan |
|---|---|---|---|
| LiteLLM / OpenRouter Proxy | ~256MB | Multi-model routing, cost/latency balancing, fallback chains, API key rotation, rate limit distribution | LiteLLM + cost tracker = kamu route request ke model termurah yang masih memenuhi SLA |
| vLLM Advanced / Speculative Decoding | ~1GB | Draft-verify pipeline, KV cache optimization, continuous batching tuning, tensor parallelism konsep | Speculative decoding + throughput chart = latency turun 30% tanpa akurasi hilang |
| AI Compliance & Audit | N/A | EU AI Act mapping, NIST AI RMF, data privacy (GDPR/PII redaction), model card documentation, bias audit | Compliance pack + automated PII check = kamu siap audit tanpa manual gathering |
| Cost & Resource Management | ~256MB | GPU utilization monitoring, auto-scaling logic, spot instance strategy, egress cost control, cache hit ROI | Prometheus + cost dashboard = kamu bisa justify infra spend berdasarkan ROI per request |
Proyek Portofolio Fase 6: Multi-Model AI Gateway + Compliance Pack — routing config, speculative decoding benchmark, EU AI Act control mapping table, PII redaction test result, cost/latency optimization report, audit trail sample.
🗺️ Roadmap Visual — Timeline 6 Bulan
| Bulan 1 | Bulan 2 | Bulan 3 | Bulan 4 | Bulan 5 | Bulan 6 |
|---|---|---|---|---|---|
| FASE 1 Serving & Quant | FASE 2 Data & Vector | FASE 3 RAG & Agent | FASE 4 MLOps & CI/CD | FASE 5 Observe & Secure | FASE 6 Scale & Comply |
| vLLM | MinIO | LangGraph | MLflow | Langfuse | LiteLLM |
| FastAPI | pgvector | Reranker | DVC | DeepEval | Speculative Dec |
| Docker | LlamaIndex | Redis Cache | GitHub Actions | Garak | AI Compliance |
| PyTorch | Unstructured | HAProxy | Evidently | NeMo Guard | Cost Tracking |
| Benchmark | Chunking Strategy | State Machine | Prompt Versioning | Red Team Report | Routing Policy |
| Portfolio 1: Inference Server + Load Test | Portfolio 2: Ingestion Pipeline + Vector Search | Portfolio 3: RAG Agent + Cache + State | Portfolio 4: AI CI/CD + DVC + MLflow | Portfolio 5: Observe Dashboard + Guardrail | Portfolio 6: Multi-Model Gateway + Compliance |
🎓 Sertifikasi yang Cocok per Fase
| Fase | Sertifikasi / Milestone | Kenapa |
|---|---|---|
| Setelah Fase 1 | NVIDIA DLI: Fundamentals of Accelerated Computing | Validasi pemahaman GPU memory management & inference basics |
| Setelah Fase 2 | Databricks: Data Engineering Associate | Pipeline reproducibility, ETL best practice, vector DB foundation |
| Setelah Fase 3 | Linux Foundation: MLOps Fundamentals | Agent architecture, caching, routing, production patterns |
| Setelah Fase 4 | MLflow Certified Practitioner (atau portfolio setara) | Experiment tracking, model registry, CI/CD for AI adalah standar industri |
| Setelah Fase 5 | OWASP AI Security & MITRE ATLAS Awareness | Guardrail, red teaming, observability, hallucination tracking |
| Setelah Fase 6 | NIST AI RMF Compliance Mapping (self-audit) | Governance, cost control, multi-model routing, audit readiness |
| Jangka Panjang | Kubernetes AI/ML Operator / Custom Platform | Scale-out, distributed inference, enterprise AI platform architecture |
⛔ Yang TIDAK Perlu Dipelajari Sekarang
Jangan Buang Waktu
- Cloud-managed AI penuh (SageMaker, Vertex AI, Azure ML) — fokus stack ini on-prem/bare-metal, kontrol penuh > kemudahan terkelola.
- Training model dari nol (pre-training/fine-tuning besar) — ini track Research/ML Engineer, bukan AI/Platform Engineer. Fokus pada serving, RAG, MLOps, security.
- Frontend/UI framework (React, Streamlit, Gradio) — kamu build backend pipeline & infrastructure, bukan dashboard. UI adalah konsumen, bukan core stack.
- Chasing SOTA paper tanpa deployment context — paper penting untuk referensi, tapi production mengutamakan latency, cost, reproducibility, dan reliability.
- Prompt engineering kursus “rahasia” — prompt adalah config, bukan skill mistis. Pelajari versioning, testing, dan guardrail.
- Entry-level data cleaning / manual labeling — otomasi ingestion, chunking, dan evaluation. Engineer tidak label data manual di production.
- CISSP / Compliance umum tanpa AI context — fokus pada EU AI Act, NIST AI RMF, dan data privacy spesifik untuk LLM/AI.
🔗 Lihat Juga
- Master Index
- llm-security-red-teaming-attack-surface-ai-layer — LLM Attack Surface, Prompt Injection, Red Teaming, Guardrails
- infrastructure-administrator — Bare-metal foundation, observability, HA, compliance
- network-security — API gateway, rate limiting, supply chain validation
- endpoint-security → BYOVD → BYOM (Bring Your Own Model) security pattern overlap
- Agentic AI & MCP Roadmap — Jalur karir engineering AI agents
- Cyber Security Blue Team Roadmap — Jalur karir Blue Team / SOC Analyst
- DevOps Roadmap — Jalur karir DevOps / SRE
- Offensive Security Roadmap — Jalur Red Team (lawannya Blue Team)
- Software Engineering Roadmap — Jalur karir software development
- Data Engineering Roadmap — Jalur karir data engineering
Roadmap AI Engineering | Fase 1 (Serving) → Fase 6 (Scale & Compliance) · 6 Bulan Homelab · Bare-Metal On-Prem Focus