Пайплайн обучения и деплоя LTV-модели
Как перевести исследовательский ноутбук с LTV-моделью в воспроизводимое обучение, хранение версий, деплой и инференс/API предсказаний?
Ответить самому
Сначала сформулируйте ответ как на собеседовании, затем откройте разбор и оцените себя.
Короткий ответ
Move reusable code from notebooks into versioned pipelines: feature extraction, training, validation, artifact registry, batch or API serving, monitoring and rollback.
Полный разбор
A notebook is good for exploration, but production needs a reproducible pipeline. The usual stages are data extraction, feature generation, train/validation split by time, model training, metric computation, artifact packaging, registry write and deployment or batch publish.
Store the model together with feature schema, preprocessing version, training data snapshot, hyperparameters, metrics and compatibility metadata. A service or batch job should load only approved artifacts. If the product needs online predictions, define an API contract and latency budget. If it needs dashboard values, a scheduled batch table may be simpler and more robust.
Monitoring should cover data freshness, row counts, feature drift, prediction distribution, model latency, error rates, realized LTV when labels mature, and business guardrails. Rollback should be possible by moving the active model pointer back to the previous approved artifact.
Теория
Production ML is a versioned data and artifact system; the model file alone is not enough.
Типичные ошибки
- Deploy code from a notebook without reproducible feature generation.
- Store a model without its feature schema and training metadata.
- Monitor task success but not data freshness or prediction drift.
- Have no rollback path for a bad model publish.
Как отвечать на собеседовании
- Separate research notebook, training pipeline and serving path.
- Say artifact plus schema plus metrics, not just pickle file.