How is LLMOps different from traditional MLOps?
mediumAnswer
- (1) Model artifacts are enormous (100+ GB) → storage / bandwidth challenge.
- (2) Fine-tuning replaces from-scratch training (base + LoRA / adapter).
- (3) Evaluation: no single metric — LLM-as-judge, human eval, rubric scoring.
- (4) Serving: KV cache management, dynamic batching, prompt caching.
- (5) Prompts are code: version, test, deploy like software.
- (6) Retrieval pipelines (RAG) as part of system.
- (7) Cost per query orders of magnitude higher.
- (8) Safety + hallucination as first-class concerns.
- (9) Non-determinism default (temperature > 0).
Check yourself — multiple choice
- Same
- 100GB+ artifacts + fine-tune vs from-scratch + LLM-judge/human/rubric eval + KV cache/batch/prompt cache + prompts=code + RAG in system + $/query high + safety/halluc first-class + non-det default
- Random
- Not real
LLMOps: bigger artifacts + fine-tune + judge eval + prompts=code + RAG + safety.
#llmops
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