Can you help me understand how Fiddler AI detects model drift compared to simpler open source monitoring tools?
Download run
Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
Can you help me understand how Fiddler AI detects model drift compared to simpler open source monitoring tools?
3 real searches · 1 real-search pass
Fiddler AI documentation model drift monitoring data drift performance monitoring explainability LLM observabilityGoogle ↗
OBSERVEDReason
Evidently AI open source monitoring data drift tests metrics documentationGoogle ↗
OBSERVEDReason
NannyML open source model monitoring concept drift performance estimation documentationGoogle ↗
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 (3)
Classes come from the query-classifier (a local Laya model). 2 of 3 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT3 real searches · gpt-6
Fiddler AI documentation model drift monitoring data drift performance monitoring explainability LLM observability Google ↗
Evidently AI open source monitoring data drift tests metrics documentation Google ↗
NannyML open source model monitoring concept drift performance estimation documentation Google ↗
- AI Observability and Security Platform · fiddler.ai
- Data sheet: Fiddler AI Observability and Security Platform · fiddler.ai
- GitHub - evidentlyai/evidently: Evidently is an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics. · github.com
- GitHub - NannyML/nannyml: nannyml: post-deployment data science in python · github.com
- nannyml/docs/landing_page.rst at main · NannyML/nannyml · github.com
- Monitoring for Enterprises · fiddler.ai
- The Leader in AI Observability for MLOps · fiddler.ai
Queries
3 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
Fiddler AI documentation model drift monitoring data drift performance monitoring explainability LLM observability OBSERVED | Google ↗ AI Mode ↗ | — | Reason | — | — | 1x of 1 | |
Evidently AI open source monitoring data drift tests metrics documentation OBSERVED | Google ↗ AI Mode ↗ | — | Reason | — | — | 1x of 1 | |
NannyML open source model monitoring concept drift performance estimation documentation OBSERVED | Google ↗ AI Mode ↗ | — | Reason | — | — | 1x of 1 |