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What is LLMOps, and how does it differ from MLOps and DevOps?
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| Aspect | DevOps | Classical MLOps | LLMOps |
|---|---|---|---|
| Main artifact | Code | Trained models + features | Prompts, model choices, RAG indexes, tools, agents (+ sometimes fine-tuned models) |
| Testing | Unit / integration tests | Accuracy on held-out data | Evals (code checks, LLM judges, human review), red-teaming |
| Change sources | Code commits | Retraining on new data | Prompt edits, provider model updates, index refreshes, tool changes |
| Cost driver | Infrastructure | Training + inference compute | Tokens per request, GPUs for self-hosting |
| Monitoring | Uptime, latency, errors | Drift, accuracy | Quality (sampled judging), safety, latency (TTFT), cost per request, drift |
| Risks | Outages, bugs | Model drift, bias | Hallucination, prompt injection, data leakage, vendor changes |
Core LLMOps capabilities
- Versioning and configuration management for prompts, models and pipelines.
- Eval pipelines with CI gates.
- Deployment and rollout strategies (canary, shadow, A/B) and rollback.
- Serving infrastructure (APIs via a gateway, or self-hosted GPUs).
- Observability: traces, metrics, cost, quality monitoring.
- Security, privacy and governance.
- Feedback loops and continuous improvement (including fine-tuning when it's justified).
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