Controlling Dependency on Third‑Party Large Language Models
summary
A practical plan for SMEs to reduce reliance on third‑party LLMs by building a continuous monitoring pipeline that detected a real performance drop when a provider changed checkpoint. Recommends concrete metrics to track (accuracy, latency, token counts, perplexity, BLEU/ROUGE, prediction confidence), a frozen golden dataset for offline validation, embedding‑space clustering for data‑drift detection, and functional drift monitoring (request volume, cost, latency); cites tools and papers such as RAG, vector databases, G-Eval, GLUE, MML and various open‑source frameworks.
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chapters (9)
- 0:00Model monitoring intro
- 2:58Model monitoring in MLOps
- 8:46On-premise vs SaaS
- 11:36LLM metrics
- 20:34Continuous monitoring
- 25:12Data drift detection
- 28:05Functional drift issues
- 31:15Model fairness
- 35:57Q&A
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