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Признаки в обучении и проде расходятся: воспроизводим ошибку на тестовом наборе

Модель проходит офлайн-тесты, но ошибается в продакшене из-за расхождения входных признаков. На примере Python разбираем, как выявить Training–Serving Skew, восстановить данные на момент прогноза и автоматизировать проверку перед релизом. Разобрать пример

habr.com/ru/companies/otus/art

#python #валидация_данных #продакшен #машинное_обучение #признаки #mlops #mlмодели #feature_store #мониторинг_моделей

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Work from anywhere | Forward Deployed Engineer

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Случилось моё микро-знакомство с миром серверов.
Получилось установить 45, добавить ключ, и
разместить статический тестовый helloworld'ный сайт.
И ещё запустить свой systemd сервис на для динамической генерации сайта.

Пока всё как-то сложно 🫣

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Reading a CSV into a #Python #Pandas data frame?

Before version 3.1, loading CSV was single-threaded and slow. My test file took 1.03s.

In 3.1, it's now multi-threaded and much faster, taking 252 ms.

However, it's still far slower than PyArrow, which took 25.5 ms.

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Fun with computer representation of numbers. I think it is the same in many languages.

Given a sensor reading as a string in milli-Cruft, convert the string to represent Cruft with up to three digits to the right of the decimal point. Take care of negative numbers too, so

0 -> 0 (or 0.000 if you like)
10 -> 0.01
-111 -> -0.111
2000 -> 2 (or 2.000 as you like)

Beware of double conversion and dividing by 1000. Turns out to be really fiddly.😩

#java #javascript #python #programming

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