Daftar Isi
- Production Architecture — Full stack dari user ke LLM
- Serving Engines — vLLM, TGI, Ollama, SGLang
- Cost Management — Teknik hemat 5-10x
- Monitoring — Latency, cost, quality tracking
- Scaling — Auto-scaling, load balancing
- CI/CD for LLM — Testing, versioning, rollback
- Series Recap — 10 Part complete
- What's Next — Roadmap lanjutan
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1. Production Architecture
Full stack: user request ke LLM response dan kembaliLLM Production Architecture
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2. Serving Engines
Optimized inference untuk production| Engine | Best For | Throughput | Ease | Key Feature |
|---|---|---|---|---|
| vLLM | High-throughput production | Tertinggi | Medium | PagedAttention, continuous batching |
| TGI (HF) | Hugging Face ecosystem | Tinggi | Easy | Token streaming, HF integration |
| Ollama | Local dev, prototyping | Medium | Very easy | 2-command setup |
| SGLang | Complex prompting | Tinggi | Medium | RadixAttention, structured gen |
| TensorRT-LLM | NVIDIA GPU max perf | Tertinggi (NVIDIA) | Hard | TensorRT optimization |
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3. Cost Management — Hemat 5-10x
Teknik yang dipakai production teams| Technique | Savings | How It Works | Trade-off |
|---|---|---|---|
| Prompt Caching | 50-90% | Cache repeated system prompts (Anthropic, OpenAI) | Only for static prefixes |
| Semantic Caching | 30-60% | Cache similar queries with embedding similarity | Slightly stale answers |
| Model Routing | 40-70% | Simple query → cheap model, complex → premium | Routing accuracy matters |
| Prompt Compression | 20-40% | LLMLingua: compress prompts, keep meaning | Slight quality loss |
| Batch Processing | 50% | Anthropic Batch API: async, half price | Higher latency |
| Self-hosted | 60-80% | Run open model (Qwen, LLaMA) on own GPU | Ops overhead, lower quality |
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4. Monitoring & Observability
Track everything: latency, cost, quality, errors| Metric | What to Track | Tools |
|---|---|---|
| Latency (p50/p95/p99) | Response time distribution | Datadog, Grafana, custom |
| Token Usage | Input/output tokens per request | API billing, LangSmith |
| Cost per Query | Total cost including infra | Custom dashboard |
| Error Rate | Failed requests, timeouts | Prometheus, PagerDuty |
| Quality Score | LLM-judge score on sample | W&B Weave, Braintrust |
| Safety Rate | % passing content filter | Guardrails AI logs |
| User Satisfaction | Thumbs up/down, NPS | In-app feedback |
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5. Seri Recap — 10 Part Complete
Perjalanan kita dari Part 1 sampai 10| Part | Topik | Key Skill |
|---|---|---|
| 1 | Apa Itu LLM | Next-token prediction, Transformer, tokenization |
| 2 | Training Pipeline | Pre-training, SFT, RLHF/DPO, RLVR |
| 3 | Prompt Engineering | Zero/few-shot, CoT, system prompts, structured output |
| 4 | RAG | Vector DB, embeddings, chunking, retrieval |
| 5 | Agents & Tools | Function calling, MCP, multi-agent, ReAct |
| 6 | Local LLMs | Ollama, quantization, hardware guide |
| 7 | Fine-tuning | LoRA, QLoRA, PEFT, data preparation |
| 8 | Evaluasi | Benchmarks, LLM-as-Judge, custom eval |
| 9 | Keamanan | Prompt injection, guardrails, anti-hallucination |
| 10 | Production | Serving, cost, monitoring, architecture |
Selamat! Seri Belajar LLM Complete!
10 Part, dari "apa itu next-token prediction" sampai production deployment. Anda sekarang memahami entire LLM stack: cara kerja, cara dipakai, cara di-deploy, dan cara diamankan. Welcome to the frontier.
What's Next? Roadmap Lanjutan
Build: 3-5 projects end-to-end (RAG chatbot, agent, fine-tuned model). Learn: Hugging Face courses, Anthropic prompt engineering docs. Practice: Kaggle LLM competitions. Deploy: Ship something to real users. Stay current: Follow Sebastian Raschka, Andrej Karpathy, Hugging Face blog. Remember: the best way to learn LLMs is to build with them.
LLM
Tech Review Desk — Seri Belajar LLM
Sumber: Sebastian Raschka, Anthropic, OpenAI, Hugging Face, LLMOrbit, DeepSeek technical reports.