How do modern teams handle continuous evaluation and monitoring of fine tuned open source models in production environments?
Download run
Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
How do modern teams handle continuous evaluation and monitoring of fine tuned open source models in production environments?
1 real search · 1 real-search pass
production monitoring continuous evaluation fine-tuned open source LLM models MLflow evaluation Evidently Arize Phoenix OpenTelemetry GenAI semantic conventionsGoogle ↗
OBSERVEDReason
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
ChatGPT real searches (1)
Classes come from the query-classifier (a local Laya model). 1 of 1 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT1 real searches · gpt-6
production monitoring continuous evaluation fine-tuned open source LLM models MLflow evaluation Evidently Arize Phoenix OpenTelemetry GenAI semantic conventions Google ↗
- Website and documentation · langfuse.com
- GitHub repository · github.com
- Website and documentation · mlflow.org
- Website and documentation · deepeval.com
- Prometheus · prometheus.io
- Grafana · grafana.com
- Arize Phoenix · mlflow.org
- Introducing DeepEval, RAGAS, and Phoenix Judges in MLflow · mlflow.org
- vLLM documentation · docs.vllm.ai
- OpenTelemetry documentation · opentelemetry.io
- MLflow GenAI evaluation · mlflow.org
- Langfuse documentation · langfuse.com
Queries
1 queries · click a column to sort