What to Monitor After an LLM Application Goes Live
Uptime dashboards will not tell you that answers have quietly become worse. A monitoring plan for quality, grounding, safety, cost and user behaviour.
Insight topic
MLOps insights
Deploying, monitoring and maintaining models after the first release. Articles in this topic are written for leaders and practitioners who need practical, evidence-based guidance.
1 article in MLOps
Topic guide
Understanding MLOps
MLOps and LLMOps keep models and AI applications dependable after launch. This topic covers monitoring, versioning, evaluation and change management for systems whose behaviour depends on data.
FAQ
Key questions about MLOps
Why is monitoring different for AI systems?
AI quality can decline without any code change, because data, users, knowledge sources and provider models change. Uptime monitoring alone does not reveal this.
What should be versioned in an LLM application?
Prompts, model versions, retrieval settings and knowledge sources, each changed through a release process that runs evaluation.
How much MLOps does a small team need?
Enough to reproduce models, deploy them reliably and detect degradation. Tooling can grow with the number of models.
Related
Services related to MLOps
Where these ideas are put into practice.
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