Deployment, артефакты and format-drift monitoring for document ML
Deployment, артефакты and format-drift monitoring for document ML
Ответить самому
Сначала сформулируйте ответ как на собеседовании, затем откройте разбор и оцените себя.
Короткий ответ
Store raw-to-structured artifacts, extraction confidence, reconciliation results, suspicious hits and model/template versions; alert on parse failures, total mismatches, distribution shifts and bank-specific error spikes.
Полный разбор
Persist enough evidence to debug every decision: document id, bank/template guess, parser version, model version, extracted rows, span confidences, normalized fields, suspicious INNs, turnover aggregates, reconciliation totals and final verdict. Raw sensitive files stay in secure storage with access control.
Operational metrics include processing latency, batch completion time, parse success rate, share of pages routed to LLM or human review, reconciliation mismatch rate, unknown-template rate, suspicious-hit rate and manual correction rate. Break these down by bank and template version.
Format drift often appears as a sudden bank-specific spike in parse failures, total mismatches, unknown templates, missing mandatory fields or support complaints. Add alerts and sampled review. In Airflow or a similar orchestrator, store model and parser artifacts, data snapshots, configuration, run ids and quality reports so a bad release can be rolled back and reproduced.
Теория
For regulated document ML, monitoring must cover extraction quality and business signals, not just service health.
Типичные ошибки
- Monitor only API latency and error rate.
- Fail to version parsers and templates.
- Aggregate metrics across banks and miss one-bank format drift.
- Store only final verdicts with no extracted evidence.
Как отвечать на собеседовании
- Name reconciliation mismatch rate as a key alert.
- Mention parser/template/model versions as artifacts.