MLOps vs LLMOps vs AIOps: One Chart Maps the 5 Key Stages
_jaydeepkarale · x · 2026-10-27
A comparison infographic breaks down how MLOps, LLMOps, and AIOps differ across five stages:
- Data: MLOps handles collection, cleaning, feature engineering and versioning (DVC); LLMOps ingests text/docs, chunks and embeds, stores vectors (Pinecone); AIOps collects logs/metrics and correlates events (Prometheus, Kafka).
- Model/Agent: training and model registry (mlflow) vs. base model selection, prompt engineering, fine-tuning/RAG (Hugging Face) vs. anomaly models and knowledge graphs (PyTorch).
- Deploy: CI/CD and canary releases vs. API/agent deployment and versioned prompts vs. pipelines, alerting and GitOps (Helm, Argo).
- Monitor: model performance and data drift vs. response quality, hallucination rate and token cost vs. system health and anomaly detection (LangSmith, Arize, OpenTelemetry).
- Feedback loop: labeling and retraining vs. prompt/RAG iteration vs. alert tuning and post-incident review.
Takeaway: the three serve different purposes—understand where each fits before going deep.
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