Анализ страницы https://2025.pycon.de/talks/
Основное Готовность: 95%
Домен
2025.pycon.de
Состояние доменного имени
?
Проверяем корректность доменного имени и наличие технических проблем на уровне домена.
Используйте для продвижения только домен второго уровня.
Отличный запоминающийся домен.
Ответ сервера
200 Успешный ответ
HTTP-код ответа и цепочка редиректов
?
Код 200 — страница доступна. Коды 3xx — редиректы (цепочки замедляют загрузку и размывают ссылочный вес). Коды 4xx/5xx — ошибки, поисковик не сможет проиндексировать страницу.
Сервер настроен корректно.
Безопасность
Сайт безопасен
Использование HTTPS и SSL-сертификат
?
HTTPS — обязательный стандарт. Google и Яндекс отдают предпочтение защищённым сайтам. Отсутствие SSL или просроченный сертификат ведут к предупреждениям в браузере и снижению позиций.
Не настроен HSTS (Strict-Transport-Security) — рекомендуется включить.
На сайте работает защищенный протокол ssl и сайт открывается по https.
Ssl-сертификат действителен до 04.10.2026 22:45:22.
Поздравляем! Сайт не содержится в реестре РКН.
Кодировка
UTF-8
Кодировка символов страницы
?
Стандарт — UTF-8. Неправильная кодировка вызывает нечитаемые символы и мешает поисковику корректно распознать текст страницы.
Указана кодировка на странице UTF-8.
Язык
en
Атрибут lang в HTML-теге
?
Атрибут lang (<html lang="ru">) сообщает поисковикам и браузерам, на каком языке написана страница. Помогает при ранжировании в региональном поиске.
Язык документа указан явно: en.
Скорость загрузки
~1,73сек
Время отклика сервера (TTFB)
?
Time To First Byte — время до получения первого байта от сервера. Норма до 200 мс. Медленный отклик ухудшает пользовательский опыт и ранжирование: Яндекс и Google учитывают скорость страниц.
Скорость загрузки сайта 1,73сек превышает 1 секунду. Желательно улучшить работу сайта!
Объем документа
189Кб
Размер HTML-кода страницы
?
Слишком большой HTML замедляет парсинг браузером и сканирование поисковым роботом. Рекомендуется не более 200 Кб.
Объем html-документа 189Кб оптимален.
Структура html-документа корректна.
Ресурсы
Ресурсы: 4
Внешние ресурсы страницы (CSS, JS, изображения)
?
Количество и тип подключённых ресурсов влияют на скорость загрузки. Большое число запросов увеличивает время рендеринга страницы.
Кол-во файлов ресурсов 4 достаточно.
Показать полный список ресурсов
| Тип | Название | Значение |
|---|---|---|
| stylesheet | /static/css/main.css | |
| stylesheet | /static/css/custom.css | |
| stylesheet | https://fonts.googleapis.com/css2?family=IBM+Plex+Sans:ital,wght@0,100;0,200;0,300;0,400;0,500;0,600;0,700;1,100;1,200;1,300;1,400;1,500;1,600;1,700&display=swap | |
| js | https://unpkg.com/@lottiefiles/lottie-player@latest/dist/lottie-player.js |
Серверные заголовки
Кол-во: 5
HTTP-заголовки ответа сервера
?
Заголовки сервера передают браузеру и поисковику служебную информацию: кеширование, безопасность (CSP, HSTS), сжатие (gzip). Правильная настройка ускоряет загрузку и повышает защищённость.
Найдены серверные заголовки 5шт. Подробнее про серверные заголовки.
Показать полный список серверных заголовков
| Ключ | Значение |
|---|---|
| Server | nginx |
| Date | Sun, 23 Aug 2026 18:57:43 GMT |
| Vary | Accept-Encoding |
| ETag | "9150ea41c2ab4decf1f30f334adea6d4" |
| x-cache-status | MISS |
CMS
Не определена
Система управления сайтом (движок)
?
CMS — это движок, на котором работает сайт (WordPress, 1C-Bitrix, Tilda и др.). Знание CMS помогает понять возможности SEO-оптимизации и подобрать подходящие инструменты. «Не определена» — вероятно, самописный сайт или нестандартная сборка.
CMS не определена. Вероятно, сайт самописный либо движок надёжно скрыт. Это не ошибка.
Веб-сервер
nginx
Программное обеспечение сервера
?
Веб-сервер — это ПО, которое отдаёт страницы посетителям (nginx, Apache, IIS, LiteSpeed и др.). Определяется по серверным заголовкам ответа (Server, X-Powered-By и т.п.). «Не определён» — сервер намеренно скрывает эти заголовки, это нормальная практика безопасности.
Сайт работает на веб-сервере nginx.
Мета-теги Готовность: 48%
Title
Talks
Заголовок страницы в браузере и поисковой выдаче
?
Title — главный SEO-заголовок страницы. Влияет на CTR в поиске и ранжирование. Оптимальная длина: 50–70 символов. Ключевые слова — ближе к началу.
Необходимо увеличить число символов в title (текущее значение мало: 7, минимум: 25, оптимально: от 40 до 45)
Дублей словоформ в title не найдено.
Description
Описание страницы в поисковой выдаче (сниппет)
?
Meta Description — текст под заголовком в выдаче. Напрямую на позиции не влияет, но влияет на CTR. Оптимальная длина: 120–160 символов.
Установите мета-тег description!
Keywords
Список ключевых слов страницы (устаревший тег)
?
Meta Keywords не учитывается Яндексом и Google для ранжирования с 2009–2012 годов. Заполнение не обязательно, но не вредит. Конкурент может использовать содержимое для анализа.
Установите мета-тег keywords!
Канонический Url
Указывает поисковику основную версию страницы
?
Canonical (rel=canonical) предотвращает проблему дублей страниц. Должен точно совпадать с URL проверяемой страницы. Неправильный canonical может передать ссылочный вес на другую страницу.
Рекомендуем прописать канонический Url.
Robots
Ошибок нет
Директивы для поисковых роботов на уровне страницы
?
Meta Robots управляет индексацией конкретной страницы: index/noindex — индексировать ли, follow/nofollow — следовать ли по ссылкам. Noindex полностью исключает страницу из поиска.
Meta-тег robots не указан. Страница свободна для индексации.
Адаптивность
width=device-width, initial-scale=1.0
Настройка масштабирования на мобильных устройствах
?
Тег viewport (<meta name="viewport">) сообщает браузеру, как масштабировать страницу на мобильных. Стандарт: width=device-width, initial-scale=1. Отсутствие — признак отсутствия мобильной версии.
Meta-тег viewport со значением-константой width=device-width задаёт ширину страницы в соответствии с размером экрана.
Meta-тег viewport со значением initial-scale=1.0 определяет масштаб 1:1, т.е. «не масштабировать».
Разметка OpenGraph
Кол-во: 6
Мета-теги для красивых превью в соцсетях
?
OpenGraph (og:title, og:description, og:image) управляет тем, как страница выглядит при репосте в социальных сетях и мессенджерах. Отсутствие OG-тегов — невзрачный превью при шеринге.
Разметка OpenGraph задана. Страница оптимизирована под социальные сети.
Показать полный список og мета-тегов
| Тип | Значение |
|---|---|
| og:title | Talks |
| og:image:secure_url | https://2025.pycon.de/static/media/social_card.png |
| og:image | https://2025.pycon.de/static/media/social_card.png |
| og:description | Join PyCon DE & PyData 2025 in Darmstadt (Frankfurt), April 23-25! Germany’s largest Python and Data Science conference with talks, workshops, and community events like DjangoGirls and sprints. Be part of the action! |
| og:url | https://2025.pycon.de/talks/ |
| og:type | article |
Все мета-теги
Кол-во: 19
Полный список мета-тегов страницы
?
Таблица всех meta-тегов, включая нестандартные. Позволяет найти опечатки, дубли и лишние теги.
Найдены мета-теги 19шт. Мета-теги не видимы для человека и предназначены для обмена информацией между веб-страницей и поисковыми системами, браузерами и другими веб-службами. С ними роботы 🤖 и устройства ведут себя более ожидаемо.
Показать полный список мета-тегов
| Тип | Название | Значение |
|---|---|---|
| name | viewport | width=device-width, initial-scale=1.0 |
| name | twitter:card | summary_large_image |
| name | twitter:site | @pyconde |
| name | twitter:title | Talks |
| name | twitter:description | |
| name | twitter:image | https://2025.pycon.de/static/media/social_card.png |
| property | og:title | Talks |
| property | image og:image:secure_url | https://2025.pycon.de/static/media/social_card.png |
| property | image og:image:secure_url | https://2025.pycon.de/static/media/social_card.png |
| property | image og:image | https://2025.pycon.de/static/media/social_card.png |
| property | image og:image | https://2025.pycon.de/static/media/social_card.png |
| property | og:description | Join PyCon DE & PyData 2025 in Darmstadt (Frankfurt), April 23-25! Germany’s largest Python and Data Science conference with talks, workshops, and community events like DjangoGirls and sprints. Be part of the action! |
| property | og:url | https://2025.pycon.de/talks/ |
| property | og:type | article |
| property | “article:publisher“ | https://2025.pycon.de |
| property | “og:site_name“ | PyConDE & PyData |
| property | “og:image:type“ | image/png |
| property | “og:image:width“ | “1200″ |
| property | “og:image:height“ | “630″ |
Оптимизация Готовность: 42%
Структура
Ошибок нет
Семантические HTML-элементы страницы
?
Проверяет наличие основных структурных элементов: nav, header, footer, main. Корректная семантическая структура помогает поисковику понять архитектуру страницы.
Структура документа корректна (теги <html> и <body> присутствуют по одному на документ).
Контент
Есть ошибки
Объём и качество текстового содержимого
?
Анализирует объём полезного текста на странице. Слишком мало — страница может считаться малополезной. Слишком много — ухудшается читаемость и восприятие.
Слова из title 0 встречаются в тексте редко. Добавьте в контент страницы слова из тега <title>!
Абзацев с текстом 164 достаточно.
Среднее число слов в абзаце 100 достаточно.
Кол-во знаков контента 130964 на странице оптимально.
Кол-во слов 19121 на странице оптимально.
Заголовки
Ошибок нет
Иерархия заголовков H1–H6
?
H1 должен быть один и содержать ключевой запрос. H2–H6 описывают подразделы. Пропуск уровней (H1 → H3) и несколько H1 — типичные ошибки, снижающие понятность страницы для поисковика.
На странице присутствуют заголовки <h2> 1. Это хорошо.
На странице присутствуют заголовки <h3> 164.
Тошнота
11,49
Насколько одно слово доминирует в тексте
?
Классическая тошнота = √(частота самого повторяющегося слова). Норма до 7–8: текст воспринимается естественно. Выше — поисковик может счесть страницу переспамленной.
Тошнота превышает норму 5. Измените текст страницы!
Академич. тошнота
63,10%
Насколько текст перенасыщен ключевыми словами
?
Академическая тошнота = (частота слова / общее количество слов) × 100%. Показывает долю конкретного слова в тексте. Норма 5–15%.
Академическая тошнота превышает норму 5-15%. Измените текст страницы!
Семантическое ядро
20
Наиболее часто встречающиеся слова на странице
?
Топ слов по частоте использования. Показывает, какие слова доминируют в тексте с точки зрения поисковика.
Контент страницы содержит осмысленный текст и слова.
Показать список слов
| Слово | Кол-во | Частота |
|---|---|---|
| python | 132 | 0,69% |
| learning | 96 | 0,50% |
| models | 68 | 0,36% |
| machine | 55 | 0,29% |
| building | 52 | 0,27% |
| language | 46 | 0,24% |
| generative | 46 | 0,24% |
| challenges | 45 | 0,24% |
| explore | 45 | 0,24% |
| engineering | 44 | 0,23% |
| practical | 42 | 0,22% |
| applications | 42 | 0,22% |
| systems | 36 | 0,19% |
| insights | 33 | 0,17% |
| through | 33 | 0,17% |
| software | 32 | 0,17% |
| session | 32 | 0,17% |
| testing | 31 | 0,16% |
| libraries | 30 | 0,16% |
| pipelines | 29 | 0,15% |
Индексация Готовность: 40%
Индексирование
Ошибок нет
Разрешено ли индексирование страницы
?
Проверяет, не закрыта ли страница от индексации через robots.txt, meta robots или X-Robots-Tag. Страница, закрытая от индексации, не появится в поисковой выдаче.
Анкоров на странице 182 оптимально. Поисковые роботы обязательно проиндексируют сайт.
Robots.txt
Найден корректный robots.txt
Файл управления сканированием сайта роботами
?
Robots.txt указывает поисковым роботам, какие страницы сканировать, а какие — нет. Ошибки в файле могут случайно закрыть важные разделы от индексации.
Robots.txt настроен корректно. Размер файла: 30489 байт. Загружен за: 1сек.
Проверяемая страница не запрещена в robots.txt.
Robots.txt доступен по постоянному адресу
Показать содержимое robots.txt
<!DOCTYPE html>
<html lang="en" class="full-landing-page">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<link rel="stylesheet" href="/static/css/main.css">
<link rel="stylesheet" href="/static/css/custom.css">
<script src="https://unpkg.com/@lottiefiles/lottie-player@latest/dist/lottie-player.js"></script>
<title>PyCon DE & PyData</title>
<link rel="preconnect" href="https://fonts.googleapis.com">
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
<link
href="https://fonts.googleapis.com/css2?family=IBM+Plex+Sans:ital,wght@0,100;0,200;0,300;0,400;0,500;0,600;0,700;1,100;1,200;1,300;1,400;1,500;1,600;1,700&display=swap"
rel="stylesheet">
<meta property="og:title"
content="PyCon DE & PyData">
<meta name="image" property="og:image:secure_url"
content="https://2025.pycon.de/static/media/social_card.png">
<meta name="image" property="og:image"
content="https://2025.pycon.de/static/media/social_card.png">
<meta property="og:description" content="Join PyCon DE & PyData 2025 in Darmstadt (Frankfurt), April 23-25! Germany’s largest Python and Data Science conference with talks, workshops, and community events like DjangoGirls and sprints. Be part of the action!">
<meta property="og:url" content="https://2025.pycon.de/">
<meta property="og:type" content="article">
<meta property=“article:publisher“ content="https://2025.pycon.de">
<meta property=“og:site_name“ content="PyConDE & PyData" />
<meta property=“og:image:type“ content="image/png" />
<meta property=“og:image:width“ content=“1200″ />
<meta property=“og:image:height“ content=“630″ />
<meta name="twitter:card" content="summary_large_image">
<meta name="twitter:site" content="@pyconde">
<meta name="twitter:title"
content="PyCon DE & PyData">
<meta name="twitter:description" content="">
<meta name="twitter:image" content="https://2025.pycon.de/static/media/social_card.png">
</head>
<body class="full-landing-page">
<label class="hamburger-menu">
<input type="checkbox" />
</label>
<aside class="sidebar">
<nav aria-label="Site Navigation">
<ul class="nav main-navigation">
<li class="navigation-link"><a href="/">Home</a></li>
<li class="navigation-link"><a href="/blog/" target="_self">Blog</a></li>
<li class="navigation-link"><a href="/talks/" target="_self">Talks</a></li>
<li class="navigation-link"><a href="/blog/lightning-talks/" target="_self">Lightning Talks</a></li>
<li class="navigation-link"><a href="https://pretalx.com/pyconde-pydata-2025/schedule/" target="_blank">Schedule</a></li>
<li class="navigation-link"><a href="/pyladies-at-pyconde-2025/" target="_self">PyLadies</a></li>
<li class="navigation-link"><a href="/sprints/" target="_self">Sprint Day</a></li>
<li class="navigation-link"><a href="/sponsors/" target="_self">Sponsors</a></li>
<li class="navigation-link"><a href="/sponsoring/" target="_self">Sponsoring</a></li>
<li class="navigation-link"><a href="/team/" target="_self">Team</a></li>
<li class="navigation-link"><a href="/venue/" target="_self">Venue</a></li>
<li class="navigation-link"><a href="/newsletter/" target="_self">Newsletter</a></li>
<li class="navigation-link"><a href="/" target="_self">----</a></li>
<li class="navigation-link"><a href="/call-for-proposals/" target="_self">Call of Proposals</a></li>
<li class="navigation-link"><a href="/financial-aid/" target="_self">Financial Aid</a></li>
</ul>
</nav>
</aside>
<div class="landing-page">
<div class="headline landing-page--intro">
<div class="landing-page--content">
<h1 class="title">PyCon DE & PyData</h1>
</div>
</div>
<div class="conference-highlights landing-page--section">
<div class="landing-page--content">
<h2>Save the Date for PyCon DE & PyData 2026!</h2>
<p><p>We are coming to Darmstadt again. See you there on 13th-17th April 2026.</p>
</p>
<ul class="image-reel">
<li><img src="/static/landing-page/venue/250424-pycon-pydata-venue1.jpg" alt="venue image"></li>
<li><img src="/static/landing-page/venue/250424-pycon-pydata-mf-FZ8_6661.jpg" alt="venue image"></li>
<li><img src="/static/landing-page/venue/250423-pycon-pydata-mf-FZ6_9771_1.jpg" alt="venue image"></li>
<li><img src="/static/landing-page/venue/250423-pycon-pydata-mf-FZ6_9774_1.jpg" alt="venue image"></li>
<li><img src="/static/landing-page/venue/250423-pycon-pydata-mf-FZ8_4517.jpg" alt="venue image"></li>
<li><img src="/static/landing-page/venue/250423-pycon-pydata-mf-FZ6_0209.jpg" alt="venue image"></li>
</ul>
</div>
</div>
<div class="cta landing-page--section">
<div class="landing-page--content">
<a href="/newsletter" class="newsletter">Subscribe to our newsletter for conference updates.</a>
</div>
</div>
<div class="content-highlights landing-page--section">
<div class="landing-page--content">
<h2>This was PyCon DE & PyData 2025 🎉</h2>
</div>
</div>
<div class="speakers landing-page--section">
<div class="landing-page--content">
<h2>Our wonderful speakers</h2>
<ul class="image-reel">
<li><img src="/static/landing-page/speakers/250424-pycon-pydata-mf-FZ6_0580.jpg" alt="speaker image"></li>
<li><img src="/static/landing-page/speakers/250423-pycon-pydata-mf-FZ8_4727.jpg" alt="speaker image"></li>
<li><img src="/static/landing-page/speakers/250424-pycon-pydata-speaker05.jpg" alt="speaker image"></li>
<li><img src="/static/landing-page/speakers/250425-pycon-pydata-mf-FZ8_7892.jpg" alt="speaker image"></li>
<li><img src="/static/landing-page/speakers/250424-pycon-pydata-mf-FZ8_6140.jpg" alt="speaker image"></li>
<li><img src="/static/landing-page/speakers/250423-pycon-pydata-mf-FZ8_4815.jpg" alt="speaker image"></li>
<li><img src="/static/landing-page/speakers/250425-pycon-pydata-mf-FZ6_1298.jpg" alt="speaker image"></li>
<li><img src="/static/landing-page/speakers/250423-pycon-pydata-mf-FZ6_0128.jpg" alt="speaker image"></li>
<li><img src="/static/landing-page/speakers/250424-pycon-pydata-speaker03.jpg" alt="speaker image"></li>
<li><img src="/static/landing-page/speakers/250424-pycon-pydata-speaker04.jpg" alt="speaker image"></li>
<li><img src="/static/landing-page/speakers/250423-pycon-pydata-mf-FZ8_4618.jpg" alt="speaker image"></li>
<li><img src="/static/landing-page/speakers/250423-pycon-pydata-mf-FZ8_4664.jpg" alt="speaker image"></li>
<li><img src="/static/landing-page/speakers/250423-pycon-pydata-mf-FZ8_4763.jpg" alt="speaker image"></li>
<li><img src="/static/landing-page/speakers/250423-pycon-pydata-mf-FZ8_4708.jpg" alt="speaker image"></li>
<li><img src="/static/landing-page/speakers/250423-pycon-pydata-mf-FZ6_0270.jpg" alt="speaker image"></li>
<li><img src="/static/landing-page/speakers/250424-pycon-pydata-mf-FZ6_0826.jpg" alt="speaker image"></li>
<li><img src="/static/landing-page/speakers/250423-pycon-pydata-mf-FZ8_4828.jpg" alt="speaker image"></li>
<li><img src="/static/landing-page/speakers/250424-pycon-pydata-mf-FZ8_6142.jpg" alt="speaker image"></li>
<li><img src="/static/landing-page/speakers/250423-pycon-pydata-mf-FZ6_0284.jpg" alt="speaker image"></li>
<li><img src="/static/landing-page/speakers/250423-pycon-pydata-mf-FZ8_4775.jpg" alt="speaker image"></li>
<li><img src="/static/landing-page/speakers/250423-pycon-pydata-mf-FZ8_4643.jpg" alt="speaker image"></li>
<li><img src="/static/landing-page/speakers/250424-pycon-pydata-mf-FZ8_6204.jpg" alt="speaker image"></li>
<li><img src="/static/landing-page/speakers/250423-pycon-pydata-mf-FZ8_4749.jpg" alt="speaker image"></li>
<li><img src="/static/landing-page/speakers/250425-pycon-pydata-mf-FZ8_8498.jpg" alt="speaker image"></li>
<li><img src="/static/landing-page/speakers/250423-pycon-pydata-mf-FZ8_5098.jpg" alt="speaker image"></li>
<li><img src="/static/landing-page/speakers/250424-pycon-pydata-speaker02.jpg" alt="speaker image"></li>
<li><img src="/static/landing-page/speakers/250425-pycon-pydata-mf-FZ8_8036.jpg" alt="speaker image"></li>
<li><img src="/static/landing-page/speakers/250423-pycon-pydata-mf-FZ8_4848.jpg" alt="speaker image"></li>
<li><img src="/static/landing-page/speakers/250424-pycon-pydata-mf-FZ8_6680.jpg" alt="speaker image"></li>
<li><img src="/static/landing-page/speakers/250424-pycon-pydata-mf-FZ8_6894.jpg" alt="speaker image"></li>
<li><img src="/static/landing-page/speakers/250425-pycon-pydata-mf-FZ8_7706.jpg" alt="speaker image"></li>
<li><img src="/static/landing-page/speakers/250425-pycon-pydata-mf-FZ8_8317.jpg" alt="speaker image"></li>
<li><img src="/static/landing-page/speakers/250423-pycon-pydata-mf-FZ8_4785.jpg" alt="speaker image"></li>
<li><img src="/static/landing-page/speakers/250425-pycon-pydata-mf-FZ8_8051.jpg" alt="speaker image"></li>
<li><img src="/static/landing-page/speakers/250423-pycon-pydata-mf-FZ8_4800.jpg" alt="speaker image"></li>
<li><img src="/static/landing-page/speakers/250425-pycon-pydata-mf-FZ8_7981.jpg" alt="speaker image"></li>
<li><img src="/static/landing-page/speakers/250424-pycon-pydata-speaker01.jpg" alt="speaker image"></li>
</ul>
</div>
</div>
<div class="workshops landing-page--section">
<div class="landing-page--content">
<h2>Workshops & Tutorials</h2>
<ul class="image-reel">
<li><img src="/static/landing-page/workshops/250424-pycon-pydata-workshop-3.jpg" alt="workshop image"></li>
<li><img src="/static/landing-page/workshops/250424-pycon-pydata-workshop-1.jpg" alt="workshop image"></li>
<li><img src="/static/landing-page/workshops/250424-pycon-pydata-workshop-2.jpg" alt="workshop image"></li>
</ul>
</div>
</div>
<div class="community-highlights landing-page--section">
<div class="landing-page--content">
<h2>The conference is you.</h2>
<ul class="image-reel">
<li><img src="/static/landing-page/community/250424-pycon-pydata-you3.jpg" alt="conference image"></li>
<li><img src="/static/landing-page/community/250423-pycon-pydata-mf-FZ6_0059.jpg" alt="conference image"></li>
<li><img src="/static/landing-page/community/250424-pycon-pydata-you1.jpg" alt="conference image"></li>
<li><img src="/static/landing-page/community/250423-pycon-pydata-mf-FZ6_0114.jpg" alt="conference image"></li>
<li><img src="/static/landing-page/community/250424-pycon-pydata-you2.jpg" alt="conference image"></li>
<li><img src="/static/landing-page/community/250422-pycon-pydata-mf-FZ8_4234.jpg" alt="conference image"></li>
<li><img src="/static/landing-page/community/250422-pycon-pydata-mf-FZ8_4071.jpg" alt="conference image"></li>
<li><img src="/static/landing-page/community/250424-pycon-pydata-you4.jpg" alt="conference image"></li>
<li><img src="/static/landing-page/community/250423-pycon-pydata-mf-FZ8_4972.jpg" alt="conference image"></li>
</ul>
</div>
</div>
<div class="organizers landing-page--section">
<div class="landing-page--content">
<h2>See you in 2026!</h2>
<ul class="image-reel">
<li><img src="/static/landing-page/organizers/250425-pycon-pydata-mf-FZ6_1724.jpg" alt="conference image"></li>
</ul>
</div>
</div>
<div class="sponsors landing-page--section">
<div class="landing-page--long-content">
<h2>Our Sponsors</h2>
<p>We are grateful to our sponsors for their support.</p>
<div class="keystone sponsor-type">
<h4>Keystone</h4>
<ul class="sponsors">
<li class="sponsor None" id="merck">
<a href="/sponsors/merck" target="_blank">
<img src="/static/media/sponsors/merck.svg" alt="Merck" />
</a>
</li>
</ul>
</div>
<div class="diamond sponsor-type">
<h4>Diamond</h4>
<ul class="sponsors">
<li class="sponsor None" id="pydantic_logfire">
<a href="/sponsors/pydantic_logfire" target="_blank">
<img src="/static/media/sponsors/pydantic_logfire.png" alt="Pydantic Logfire" />
</a>
</li>
</ul>
</div>
<div class="platinum sponsor-type">
<h4>Platinum</h4>
<ul class="sponsors">
<li class="sponsor None" id="quantco">
<a href="/sponsors/quantco" target="_blank">
<img src="/static/media/sponsors/quantco.svg" alt="QuantCo" />
</a>
</li>
<li class="sponsor None" id="eon">
<a href="/sponsors/eon" target="_blank">
<img src="/static/media/sponsors/eon.svg" alt="E.ON" />
</a>
</li>
<li class="sponsor scale-80" id="zeiss">
<a href="/sponsors/zeiss" target="_blank">
<img src="/static/media/sponsors/zeiss.svg" alt="ZEISS" />
</a>
</li>
<li class="sponsor scale-70" id="exasol">
<a href="/sponsors/exasol" target="_blank">
<img src="/static/media/sponsors/Exasol_Logo_2025_Dark.svg" alt="Exasol" />
</a>
</li>
<li class="sponsor None" id="rosenxt">
<a href="/sponsors/rosenxt" target="_blank">
<img src="/static/media/sponsors/rosenxt.svg" alt="Rosenxt" />
</a>
</li>
<li class="sponsor scale-90" id="gurobi">
<a href="/sponsors/gurobi" target="_blank">
<img src="/static/media/sponsors/gurobi.svg" alt="Gurobi Optimization" />
</a>
</li>
<li class="sponsor scale-90" id="jetbrains">
<a href="/sponsors/jetbrains" target="_blank">
<img src="/static/media/sponsors/JetBrains-logo-RGB.svg" alt="JetBrains" />
</a>
</li>
<li class="sponsor scale-90" id="mostlyai">
<a href="/sponsors/mostlyai" target="_blank">
<img src="/static/media/sponsors/MOSTLY.AI_logo_full_colourGradient_RGB.svg" alt="MOSTLY AI" />
</a>
</li>
<li class="sponsor None" id="blueyonder">
<a href="/sponsors/blueyonder" target="_blank">
<img src="/static/media/sponsors/blueyonder.svg" alt="Blue Yonder" />
</a>
</li>
<li class="sponsor None" id="snowflake">
<a href="/sponsors/snowflake" target="_blank">
<img src="/static/media/sponsors/snowflake-logo-color-rgb.svg" alt="Snowflake" />
</a>
</li>
<li class="sponsor scale-80" id="auth0">
<a href="/sponsors/auth0" target="_blank">
<img src="/static/media/sponsors/Auth0_Shield Lockup_Black_RGB.svg" alt="Auth0" />
</a>
</li>
<li class="sponsor scale-80" id="redis">
<a href="/sponsors/redis" target="_blank">
<img src="/static/media/sponsors/redis.svg" alt="Redis" />
</a>
</li>
<li class="sponsor None" id="hopsworks">
<a href="/sponsors/hopsworks" target="_blank">
<img src="/static/media/sponsors/hopsworks.svg" alt="Hopsworks" />
</a>
</li>
</ul>
</div>
<div class="gold sponsor-type">
<h4>Gold</h4>
<ul class="sponsors">
<li class="sponsor None" id="rosen">
<a href="/sponsors/rosen" target="_blank">
<img src="/static/media/sponsors/rosen_c.svg" alt="Rosen" />
</a>
</li>
<li class="sponsor None" id="sovanta">
<a href="/sponsors/sovanta" target="_blank">
<img src="/static/media/sponsors/sovanta.svg" alt="sovanta" />
</a>
</li>
<li class="sponsor scale-80" id="experteach">
<a href="/sponsors/experteach" target="_blank">
<img src="/static/media/sponsors/experteach.svg" alt="ExperTeach" />
</a>
</li>
<li class="sponsor scale-80" id="kraken">
<a href="/sponsors/kraken" target="_blank">
<img src="/static/media/sponsors/Stacked_ Kraken_logo_Dark_RGB.svg" alt="Kraken" />
</a>
</li>
<li class="sponsor scale-80" id="prokube">
<a href="/sponsors/prokube" target="_blank">
<img src="/static/media/sponsors/prokube-logo-positive.svg" alt="prokube" />
</a>
</li>
<li class="sponsor None" id="heidelberger-druckmaschinen">
<a href="/sponsors/heidelberger-druckmaschinen" target="_blank">
<img src="/static/media/sponsors/heidelberger-druckmaschinen.png" alt="Heidelberger Druckmaschinen AG" />
</a>
</li>
<li class="sponsor scale-80" id="vonage">
<a href="/sponsors/vonage" target="_blank">
<img src="/static/media/sponsors/VonagePOE_Primary.svg" alt="Vonage" />
</a>
</li>
<li class="sponsor scale-80" id="aleph-alpha">
<a href="/sponsors/aleph-alpha" target="_blank">
<img src="/static/media/sponsors/AlephAlpha_logo-ver_BasaltBlack_rgb.svg" alt="Aleph Alpha" />
</a>
</li>
<li class="sponsor None" id="data-santander">
<a href="/sponsors/data-santander" target="_blank">
<img src="/static/media/sponsors/data-santander.svg" alt="Data Santander" />
</a>
</li>
<li class="sponsor None" id="deepl">
<a href="/sponsors/deepl" target="_blank">
<img src="/static/media/sponsors/DeepL-RGB .svg" alt="DeepL" />
</a>
</li>
</ul>
</div>
<div class="silver sponsor-type">
<h4>Silver</h4>
<ul class="sponsors">
<li class="sponsor scale-80" id="inovex">
<a href="/sponsors/inovex" target="_blank">
<img src="/static/media/sponsors/inovex.svg" alt="inovex GmbH" />
</a>
</li>
<li class="sponsor scale-70" id="lidl">
<a href="/sponsors/lidl" target="_blank">
<img src="/static/media/sponsors/lidl.svg" alt="Lidl Stiftung & Co. KG" />
</a>
</li>
<li class="sponsor None" id="schwaebisch_hall">
<a href="/sponsors/schwaebisch_hall" target="_blank">
<img src="/static/media/sponsors/bausparkasse-schwaebisch-hall.svg" alt="Bausparkasse Schwäbisch Hall AG" />
</a>
</li>
<li class="sponsor None" id="rewe">
<a href="/sponsors/rewe" target="_blank">
<img src="/static/media/sponsors/REWEGr_RGB_mitSchutzraum.svg" alt="REWE Group" />
</a>
</li>
<li class="sponsor None" id="quansight">
<a href="/sponsors/quansight" target="_blank">
<img src="/static/media/sponsors/quansight.svg" alt="Quansight" />
</a>
</li>
<li class="sponsor scale-80" id="items">
<a href="/sponsors/items" target="_blank">
<img src="/static/media/sponsors/items.svg" alt="items GmbH & Co. KG" />
</a>
</li>
<li class="sponsor None" id="energy_meteo">
<a href="/sponsors/energy_meteo" target="_blank">
<img src="/static/media/sponsors/em.svg" alt="energy & meteo systems" />
</a>
</li>
<li class="sponsor None" id="cologne_intelligence">
<a href="/sponsors/cologne_intelligence" target="_blank">
<img src="/static/media/sponsors/cologne_intelligence.svg" alt="Cologne Intelligence" />
</a>
</li>
<li class="sponsor None" id="effect_data_solutions">
<a href="/sponsors/effect_data_solutions" target="_blank">
<img src="/static/media/sponsors/effect_data_solutions.svg" alt="&effect data solutions GmbH" />
</a>
</li>
<li class="sponsor None" id="innoflair">
<a href="/sponsors/innoflair" target="_blank">
<img src="/static/media/sponsors/innoflair.svg" alt="Innoflair" />
</a>
</li>
<li class="sponsor None" id="quattro_research">
<a href="/sponsors/quattro_research" target="_blank">
<img src="/static/media/sponsors/quattro_research.svg" alt="quattro research GmbH" />
</a>
</li>
<li class="sponsor scale-70" id="beauhurst">
<a href="/sponsors/beauhurst" target="_blank">
<img src="/static/media/sponsors/beauhurst.svg" alt="Beauhurst" />
</a>
</li>
<li class="sponsor None" id="aboenergy">
<a href="/sponsors/aboenergy" target="_blank">
<img src="/static/media/sponsors/abo_energy.svg" alt="ABO Energy" />
</a>
</li>
<li class="sponsor None" id="sunnic-ligthhouse">
<a href="/sponsors/sunnic-ligthhouse" target="_blank">
<img src="/static/media/sponsors/Logo-SunnicLighthouse-2farbig.svg" alt="Sunnic Lighthouse GmbH" />
</a>
</li>
<li class="sponsor None" id="tegtmeier-inkubator">
<a href="/sponsors/tegtmeier-inkubator" target="_blank">
<img src="/static/media/sponsors/Tegtmeier_Inkubator_Logo_Green-Black.svg" alt="Tegtmeier Inkubator GmbH" />
</a>
</li>
<li class="sponsor None" id="edih-aics">
<a href="/sponsors/edih-aics" target="_blank">
<img src="/static/media/sponsors/EDIH_AICS_Logo.svg" alt="EDIH-AICS" />
</a>
</li>
</ul>
</div>
<div class="institutional-sponsors sponsor-type">
<h4>Institutional Partners</h4>
<ul class="sponsors">
<li class="sponsor None" id="pysv">
<a href="/sponsors/pysv" target="_blank">
<img src="/static/media/sponsors/pysv.svg" alt="Python Softwareverband e.V." />
</a>
</li>
<li class="sponsor None" id="numfocus">
<a href="/sponsors/numfocus" target="_blank">
<img src="/static/media/sponsors/numfocus.svg" alt="NumFOCUS" />
</a>
</li>
<li class="sponsor None" id="pioneershub">
<a href="/sponsors/pioneershub" target="_blank">
<img src="/static/media/sponsors/pioneershub.svg" alt="Pioneers Hub" />
</a>
</li>
</ul>
</div>
<div class="diversity-sponsors sponsor-type">
<h4>Diversity Sponsors</h4>
<ul class="sponsors">
<li class="sponsor scale-80" id="psf">
<a href="/sponsors/psf" target="_blank">
<img src="/static/media/sponsors/psf.png" alt="Python Software Foundation" />
</a>
</li>
<li class="sponsor scale-80" id="eps">
<a href="/sponsors/eps" target="_blank">
<img src="/static/media/sponsors/eps.png" alt="EuroPython Society (EPS)" />
</a>
</li>
</ul>
</div>
<div class="community-partners sponsor-type">
<h4>Community Partners</h4>
<ul class="sponsors">
<li class="sponsor None" id="applied-ai-institute">
<a href="/sponsors/applied-ai-institute" target="_blank">
<img src="/static/media/sponsors/appliedAI_horizontal_rgb_RZ.svg" alt="appliedAI Institute for Europe gGmbH" />
</a>
</li>
<li class="sponsor None" id="hessian_ai">
<a href="/sponsors/hessian_ai" target="_blank">
<img src="/static/media/sponsors/hessian.AI-vertical.svg" alt="Hessian AI" />
</a>
</li>
<li class="sponsor None" id="ulb">
<a href="/sponsors/ulb" target="_blank">
<img src="/static/media/sponsors/ulb_logo_schriftzug_tuerkis_rgb.svg" alt="ULB - University and State Library Darmstadt" />
</a>
</li>
<li class="sponsor scale-70" id="pyladies_suedwest">
<a href="/sponsors/pyladies_suedwest" target="_blank">
<img src="/static/media/sponsors/pyladies-suedwest.svg" alt="PyLadies Südwest" />
</a>
</li>
<li class="sponsor scale-90" id="pyladies_hamburg">
<a href="/sponsors/pyladies_hamburg" target="_blank">
<img src="/static/media/sponsors/pyladies-hamburg.svg" alt="PyLadies Hamburg" />
</a>
</li>
<li class="sponsor scale-70" id="pyladies_berlin">
<a href="/sponsors/pyladies_berlin" target="_blank">
<img src="/static/media/sponsors/pyladies-berlin.png" alt="PyLadies Berlin" />
</a>
</li>
<li class="sponsor None" id="pyladies_munich">
<a href="/sponsors/pyladies_munich" target="_blank">
<img src="/static/media/sponsors/pyladies-munich-w.png" alt="PyLadies Munich" />
</a>
</li>
<li class="sponsor None" id="pydata_suedwest">
<a href="/sponsors/pydata_suedwest" target="_blank">
<img src="/static/media/sponsors/pydata-suedwest.svg" alt="PyData Südwest" />
</a>
</li>
<li class="sponsor None" id="pydata_rhein_main">
<a href="/sponsors/pydata_rhein_main" target="_blank">
<img src="/static/media/sponsors/pydata-rhein-main.svg" alt="PyData Rhein-Main" />
</a>
</li>
<li class="sponsor None" id="pydata_berlin">
<a href="/sponsors/pydata_berlin" target="_blank">
<img src="/static/media/sponsors/pydata-berlin.svg" alt="PyData Berlin" />
</a>
</li>
<li class="sponsor None" id="djangogirls">
<a href="/sponsors/djangogirls" target="_blank">
<img src="/static/media/sponsors/djangogirls-logo.svg" alt="DjangoGirls" />
</a>
</li>
<li class="sponsor None" id="hessian_ai">
<a href="/sponsors/hessian_ai" target="_blank">
<img src="/static/media/sponsors/AISR_Logo_RGB.svg" alt="AISR" />
</a>
</li>
<li class="sponsor scale-80" id="probably_private">
<a href="/sponsors/probably_private" target="_blank">
<img src="/static/media/sponsors/probably_private_circle.png" alt="Probably Private" />
</a>
</li>
<li class="sponsor None" id="spacy">
<a href="/sponsors/spacy" target="_blank">
<img src="/static/media/sponsors/spacy_logo_blue.svg" alt="spaCy" />
</a>
</li>
<li class="sponsor None" id="women-in-ai">
<a href="/sponsors/women-in-ai" target="_blank">
<img src="/static/media/sponsors/WAI_Logo_Black.svg" alt="Women in AI" />
</a>
</li>
</ul>
</div>
</div>
</div>
</div>
<section class="legal-links">
<div class="content">
<nav class="links">
<ul>
<li><a href="/code-of-conduct">Code of Conduct</a></li>
<li><a href="/privacy-policy">Privacy Policy</a></li>
<li><a href="/imprint">Imprint</a></li>
</ul>
</nav>
</div>
</section>
</body>
</html>
Sitemap
Кол-во: 0
XML-карта сайта для поисковиков
?
Sitemap.xml помогает поисковику быстрее находить и индексировать страницы. Особенно важен для крупных сайтов и новых страниц, на которые ещё нет входящих ссылок.
Robots.txt не содержит ссылку на карту сайта. Рекомендуется добавить карту сайта и указать ссылку на нее в robots.txt.
Внутренние ссылки
Кол-во: 181
Ссылки на другие страницы своего сайта
?
Внутренние ссылки распределяют ссылочный вес между страницами и помогают поисковику обходить сайт. Пустые анкоры и ссылки на запрещённые robots.txt страницы — типичные ошибки.
Внутренних ссылок на странице 181 слишком много. Проведите оптимизацию сайта!
Внутренние ссылки не запрещены к индексации в robots.txt.
Показать первые 100 внутренних ссылок
| Url | Анкор | Состояние |
|---|---|---|
| / |
Home
|
|
| /blog/ |
Blog
|
|
| /talks/ |
Talks
|
|
| /blog/lightning-talks/ |
Lightning Talks
|
|
| /pyladies-at-pyconde-2025/ |
PyLadies
|
|
| /sprints/ |
Sprint Day
|
|
| /sponsors/ |
Sponsors
|
|
| /sponsoring/ |
Sponsoring
|
|
| /team/ |
Team
|
|
| /venue/ |
Venue
|
|
| /newsletter/ |
Newsletter
|
|
| /talks/ |
----
|
|
| /call-for-proposals/ |
Call of Proposals
|
|
| /financial-aid/ |
Financial Aid
|
|
| /talks/CTUEJX/ |
<h3 class="talk-title">
Streaming at 30,000 Feet: A Real-Time Journey from APIs to Stream Processing
</h3>
<h4>
Felix Leon Buck
</h4>
<div class="track">Programming & Software Engineering</div>
<p class="talk-abstract">Traditional API architectures face significant challenges in environments where repetitive and frequent requests are required to retrieve data updates. These request-response mechanisms introduce latency, as clients must continually query the server to check for changes, often receiving redundant or outdated information. This approach leads to increased network overhead, inefficient use of server resources and diminished scalability as the number of clients or requests grows. Additionally, frequent requests expand the attack surface, requiring security measures to mitigate risks such as (un-)authorised access, rate limiting and query sanitisation. Managing all of these inherent problem results in increasingly complex systems to maintain and improve while putting considerable implementation effort onto the customer.
Join to find out how transitioning to a streaming architecture can address these issues by providing proactive, event-based data delivery, reducing latency, minimising redundant processing, enhancing scalability and simplifying security management.</p>
|
|
| /talks/VKYDBD/ |
<h3 class="talk-title">
Building Bare-Bones Game Physics in Rust with Python Integration
</h3>
<h4>
Sam Kaveh
</h4>
<div class="track">Rust</div>
<p class="talk-abstract">Learn how to build a minimalist game physics engine in Rust and make it accessible to Python developers using PyO3. This talk explores fundamental concepts like collision detection and motion dynamics while focusing on Python integration for scripting and testing. Ideal for developers interested in combining Rust’s performance with Python’s ease of use to create lightweight and efficient tools for games or simulations.</p>
|
|
| /talks/FUX3FR/ |
<h3 class="talk-title">
Conquering PDFs: document understanding beyond plain text
</h3>
<h4>
Ines Montani
</h4>
<div class="track">Natural Language Processing & Audio (incl. Generative AI NLP)</div>
<p class="talk-abstract">NLP and data science could be so easy if all of our data came as clean and plain text. But in practice, a lot of it is hidden away in PDFs, Word documents, scans and other formats that have been a nightmare to work with. In this talk, I'll present a new and modular approach for building robust document understanding systems, using state-of-the-art models and the awesome Python ecosystem. I'll show you how you can go from PDFs to structured data and even build fully custom information extraction pipelines for your specific use case.</p>
|
|
| /talks/BJKSGK/ |
<h3 class="talk-title">
Decoding Topics: A Comparative Analysis of Python’s Leading Topic Modeling Libraries Using Climate C
</h3>
<h4>
Dr. Lisa Andreevna Chalaguine
</h4>
<div class="track">Natural Language Processing & Audio (incl. Generative AI NLP)</div>
<p class="talk-abstract">Topic modelling has come a long way, evolving from traditional statistical methods to leveraging advanced embeddings and neural networks. Python’s diverse library ecosystem includes tools like Latent Dirichlet Allocation (LDA) using gensim, Top2Vec, BERTopic, and Contextualized Topic Models (CTM). This talk evaluates these popular approaches using a dataset of UK climate change policies, considering use cases relevant to organisations like DEFRA (Department for Environment, Food & Rural Affairs). The analysis explores real-time integration, dynamic topic modelling over time, adding new documents, and retrieving similar ones. Attendees will learn the strengths, limitations, and practical applications of each library to make informed decisions for their projects.</p>
|
|
| /talks/MNTFRG/ |
<h3 class="talk-title">
Using Causal thinking to make Media Mix Modeling
</h3>
<h4>
Carlos Trujillo
</h4>
<div class="track">PyData & Scientific Libraries Stack</div>
<p class="talk-abstract">In today's data-driven landscape, understanding causal relationships is essential for effective marketing strategies. This talk will explore the link between Bayesian causal thinking and media mix modeling, utilizing Directed Acyclic Graphs (DAGs), Structural Causal Models (SCMs), and the Data Generation Process (DGP).
We will examine how DAGs represent causal assumptions, how SCMs define relationships in media mix models, and how to implement these models within a Bayesian framework. By using media mix models as causal inference tools, we can estimate counterfactuals and causal effects, offering insights into the effectiveness of media investments.</p>
|
|
| /talks/CMTKZS/ |
<h3 class="talk-title">
Deploying Synchronous and Asynchronous Django Applications for Hobby Projects
</h3>
<h4>
melhin
</h4>
<div class="track">Django & Web</div>
<p class="talk-abstract">Simplify deploying hybrid Django applications with synchronous views and asynchronous apps. This session covers ASGI support, Docker containerization, and Kamal for seamless, zero-downtime deployments on single-server setups, ideal for hobbyists and small-scale projects.</p>
|
|
| /talks/PDBAXQ/ |
<h3 class="talk-title">
pytest - simple, rapid and fun testing with Python
</h3>
<h4>
Florian Bruhin
</h4>
<div class="track">Testing</div>
<p class="talk-abstract">The pytest tool offers a rapid and simple way to write tests for your Python code. This training gives an introduction with exercises to some distinguishing features, such as its assertions, marks and fixtures.
Despite its simplicity, pytest is incredibly flexible and configurable. We'll look at various configuration options as well as the plugin ecosystem around pytest.</p>
|
|
| /talks/CVMPVG/ |
<h3 class="talk-title">
Guardians of the Code: Safeguarding Machine Learning Models in a Climate Tech World
</h3>
<h4>
Doreen Sacker
</h4>
<div class="track">MLOps & DevOps</div>
<p class="talk-abstract">LLMs, Machine learning and AI are everywhere, yet their security is often overlooked, leaving your systems vulnerable to serious attacks. What happens when someone tampers with your model’s input, poisons your training data, or steals your model?
In this talk, I’ll explore these risks through the lens of the OWASP Machine Learning Security Top 10 using relatable, real-world examples from the climate tech world. I’ll explain how these attacks happen, their impact, and why they matter to you as a Python developer, data scientist, or data engineer.
You’ll learn practical ways to defend your models and pipelines, ensuring they’re robust against adversarial forces. Bridging theory and practice, you'll leave equipped with insights and strategies to secure your machine learning systems, whether you’re training models or deploying them in production. By the end, you’ll have a solid understanding of the risks, a toolkit of best practices, and maybe even a new perspective on how important security is everywhere.</p>
|
|
| /talks/WCDPLP/ |
<h3 class="talk-title">
FastHTML vs. Streamlit - The Dashboarding Face Off
</h3>
<h4>
Tilman Krokotsch
</h4>
<div class="track">Django & Web</div>
<p class="talk-abstract">In the right corner, we have the go-to dashboarding solution for showcasing ML models or visualizing data, **STREAMLIT** (\*crowd cheers\*). Simple yet powerful, it defends the throne of Python dashboarding, but have you ever tried to create complex interactions with it? Things like drill-downs or logins, can make your control flow become messy really quick (\*crowd nods knowlingly\*).
And in the left corner, the new contender in the arena of Python web frameworks which, according to its docs, "*excels at building dashboards*", **FastHTML** (\*crowd whoops\*). We will see if this is true, in the **ultimate dashboarding face off** (\*crowd gasps\*). By building the same dashboard, step by step, in both frameworks, investigate their strengths and weaknesses, we will see which framework can claim the crown.</p>
|
|
| /talks/RAHBEP/ |
<h3 class="talk-title">
Forecast of Hourly Train Counts on Rail Routes Affected by Construction Work
</h3>
<h4>
Sebastian Folz, Dr Maren Westermann
</h4>
<div class="track">Machine Learning & Deep Learning & Statistics</div>
<p class="talk-abstract">Construction work in national railroad networks often disrupts train traffic, making it vital to estimate hourly train numbers for effective re-routing. Traditionally managed by humans, this process has been automated due to staff shortages and demographic changes. DB Systel GmbH, Deutsche Bahn's IT provider, leveraged machine learning and artificial intelligence to estimate train traffic during construction. Using Python and frameworks like Pandas, scikit-learn, NumPy, PyTorch and Polars, their solution demonstrated significant benefits in performance and efficiency.</p>
|
|
| /talks/AGY8CT/ |
<h3 class="talk-title">
Serverless Orchestration: Exploring the Future of Workflow Automation
</h3>
<h4>
Tim Bossenmaier
</h4>
<div class="track">Programming & Software Engineering</div>
<p class="talk-abstract">Orchestration is a typical challenge in the data engineering world. Scheduling your data transformation jobs via CRON-jobs is cumbersome and error-prone. Furthermore, with an increasing number of jobs to manage it gets in-oversee able. Tools like Apache Airflow, Dagster, Luigi, and Prefect are known for addressing these challenges but often require additional resources or investment. With the advent of serverless orchestration tools, many of these disadvantages are mitigated, offering a more streamlined and cost-effective solution.
This session provides a comprehensive overview of combining serverless architecture with orchestration. We will start by defining the core concepts of orchestration and serverless technologies and discuss the benefits of integrating them. The talk will then analyze solutions available in the cloud vendor space. Attendees will leave with a well-rounded understanding of the tools and strategies available in serverless orchestration.</p>
|
|
| /talks/TYXMZC/ |
<h3 class="talk-title">
Guiding data minds: how mentoring transforms careers for both sides
</h3>
<h4>
Anastasia Karavdina
</h4>
<div class="track">Community & Diversity</div>
<p class="talk-abstract">Mentorship is a powerful way to shape careers while building meaningful connections in the data field. In this talk, I’ll share my journey as a professional mentor, what the role entails, and the impact it has on both mentees and mentors. Learn how mentorship drives growth, fosters innovation, and creates value for the data community—and why you should consider stepping into this rewarding role.</p>
|
|
| /talks/GUEAHT/ |
<h3 class="talk-title">
Offline Disaster Relief Coordination with OpenStreetMap and FastAPI
</h3>
<h4>
Jannis Lübbe
</h4>
<div class="track">Infrastructure - Hardware & Cloud</div>
<p class="talk-abstract">In natural disaster scenarios, reliable communication is crucial. This talk presents a solution for disaster relief coordination using OpenStreetMap vector maps hosted on a local device in the emergency vehicle with FastAPI, ensuring functionality without an internet connection. By integrating a database of post codes and street names, and leveraging a LORAWAN gateway to receive positional data and water levels, this system ensures access to critical information even in blackout situations.</p>
|
|
| /talks/P9GRZU/ |
<h3 class="talk-title">
Quiet on Set: Building an On-Air Sign with Open Source Technologies
</h3>
<h4>
Danica Fine
</h4>
<div class="track">Infrastructure - Hardware & Cloud</div>
<p class="talk-abstract">Learn how to build a custom On-Air sign using Apache Kafka®, Apache Flink®, and Apache Iceberg™! See how to capture events like Zoom meetings and camera usage with Python, process data with FlinkSQL, analyze trends using Iceberg, and bring it all together with a practical IoT project that easily scales out.</p>
|
|
| /talks/S8MUBF/ |
<h3 class="talk-title">
Why Exceptions Are Just Sophisticated Gotos - and How to Move Beyond
</h3>
<h4>
Florian Wilhelm
</h4>
<div class="track">Programming & Software Engineering</div>
<p class="talk-abstract">"Why Exceptions Are Just Sophisticated Gotos - and How to Move Beyond" explores a common programming tool with a fresh perspective. While exceptions are a key feature in Python and other languages, they share surprising similarities with the notorious goto statement. This talk examines those parallels, the problems exceptions can create, and practical alternatives for better code. Attendees will gain a clear understanding of modern programming concepts and the evolution of programming.</p>
|
|
| /talks/FGEUJJ/ |
<h3 class="talk-title">
Conformal Prediction: uncertainty quantification to humanise models
</h3>
<h4>
Vincenzo Ventriglia
</h4>
<div class="track">Machine Learning & Deep Learning & Statistics</div>
<p class="talk-abstract">Quantifying model uncertainties is critical to improve model reliability and make sound decisions. Conformal Prediction is a framework for uncertainty quantification that provides mathematical guarantees of true outcome coverage, allowing more informed decisions to be made by stakeholders</p>
|
|
| /talks/UVPALT/ |
<h3 class="talk-title">
PDFs - When a thousand words are worth more than a picture (or table).
</h3>
<h4>
Caio Benatti Moretti
</h4>
<div class="track">Generative AI</div>
<p class="talk-abstract">PDF, a must-have in RAG systems, ensures visual fidelity across platforms and devices, at the expense of compromising what would be the core condition for computers to properly process and interpret text: semantics. That means any logical arrangement of text, upon rendering, explodes into dummy visual shards of data that literally portrait the bigger picture for the human eye to perceive, but no longer convey the information computers should grasp. Such a bottleneck already makes proper ingestion of text-only documents a big challenge, let alone when tables or figures come into play, the ultimate nightmare for PDF parsers, not to say developers. The rest you must have already foreseen: a RAG system barfing unreliable knowledge from bad chunks (based on regular PDF parsing), if those ever get to be retrieved from a vector database. In this talk you can gather some vision-driven insights on how to leverage the strengths of PDF and language models towards good chunks to be ingested. Or, in other words, how multimodal models can go beyond trivial reverse engineering by decomposing tables into its building blocks, in plain language, as how those would be explained to another human; or better yet, as how humans would ask questions about such pieces of knowledge. And from such a strategy, we transfer the same rationale to figures. Come along, gather some insights, and get inspired to break down tables and figures from your own PDFs, and to improve retrieval in your RAG systems.</p>
|
|
| /talks/JM3G8S/ |
<h3 class="talk-title">
Why E.ON Loves Python
</h3>
<h4>
Christer Friberg
</h4>
<div class="track">MLOps & DevOps</div>
<p class="talk-abstract">Join me as I share my 20-year journey with Python and its pivotal role at E.ON. Discover how we transitioned fully to Python, streamlined our development framework, and embraced MLOps principles. Learn about some of our AI projects, including image analysis and real-time inference, and our steps towards open-sourcing code to foster innovation in the energy sector. Explore why Python is our go-to language for data science and collaboration.</p>
|
|
| /talks/BR3D83/ |
<h3 class="talk-title">
From Algorithm to Action: Building a DIY Distributed Trading Platform with Open Source
</h3>
<h4>
Eugen Geist
</h4>
<div class="track">Programming & Software Engineering</div>
<p class="talk-abstract">In this talk, we'll explore how you can implement your own distributed system for algorithmic trading leveraging the power of open source without being dependent on trading bot providers.
We will discuss different challenges occurring in HFT inter alia processing massive amounts of data with low latency and reliable risk control and how to solve them. Furthermore we will touch on the topic of regulatory requirements in trading.
These challenges will be addressed through a distributed system implemented in Python, utilizing Kafka for real-time data streaming and PostgreSQL for persistent storage. We will examine approaches to decouple the components to re-use and scale them across different markets.
Cryptocurrency markets are used as a proving ground for the PoC due to easy availability for everyone.</p>
|
|
| /talks/KZKT9W/ |
<h3 class="talk-title">
Duplicate Code Dilemma: Unlocking Automation with Open Source!
</h3>
<h4>
Raana Saheb-Nassagh
</h4>
<div class="track">Programming & Software Engineering</div>
<p class="talk-abstract">"Don't Repeat Yourself" – a phrase that we have all heard many times. In this talk, we will have an overview how to deal with code duplication and how open-source template libraries such as Copier can assist us in managing similarly structured repositories. Furthermore, we will explore how code updates can be automated with the help of open-source libraries like Renovate Bot. By the end of this session, you will gain insights into these solutions while also questioning whether they truly eliminate repetition or merely contribute to another cycle of automation.</p>
|
|
| /talks/DPAPUA/ |
<h3 class="talk-title">
GitMLOps – How we are managing 100+ ML pipelines in AWS SageMaker
</h3>
<h4>
Bogdan Girman
</h4>
<div class="track">MLOps & DevOps</div>
<p class="talk-abstract">Scaling machine learning pipelines is no small feat - especially when you’re managing over 100 of them on AWS SageMaker. In this talk, I’ll take you behind the scenes of how our team at idealo built a Git-based MLOps framework that powers millions of real-time recommendations every minute.
I’ll share the challenges we faced, the solutions we implemented, and the lessons we learned while streamlining model versioning, deployment, and monitoring. This session is packed with actionable takeaways for ML engineers, data scientists, and DevOps professionals looking to simplify their MLOps workflows and operate efficiently at scale.
Whether you’re running a handful of pipelines or preparing to scale up, this talk will equip you with the tools and strategies to tackle MLOps with confidence.</p>
|
|
| /talks/7CXSPN/ |
<h3 class="talk-title">
Reinventing Streamlit
</h3>
<h4>
Malte Klemm
</h4>
<div class="track">Django & Web</div>
<p class="talk-abstract">Dreaming of creating sleek, interactive web apps with just Python? Streamlit is great for dashboards, but what if your needs go beyond that? Discover how Reflex.dev, a cutting-edge full-stack Python framework, lets you level up from dashboards to full-fledged web apps!</p>
|
|
| /talks/EN3QPQ/ |
<h3 class="talk-title">
Are LLMs the answer to all our problems?
</h3>
<h4>
Dr. Maria Börner
</h4>
<div class="track">Ethics & Privacy</div>
<p class="talk-abstract">Generative AI models have shaken up the German market. Since the release of ChatGPT, AI is available and usable for everyone. The number of ChatGPT-based agents is growing rapidly, but concerns about privacy, copyright and ethics remain. Regulation and ethical AI go hand in hand, but are often seen as barriers. The presentation will cover the different aspects of ethics and how they are addressed by regulation. It will give an overview of how to use large language models in a safe and practical way. This won't only address the various ethical issues, but also convince your next customer to invest in your AI-based product.</p>
|
|
| /talks/JA9NFW/ |
<h3 class="talk-title">
Beyond Agents: What AI Strategy Really Needs in 2025
</h3>
<h4>
Alexander CS Hendorf
</h4>
<div class="track">Others</div>
<p class="talk-abstract">Artificial intelligence is no longer confined to models and APIs—it now shapes systems, hardware, and real-world agents. In this talk, I reflect on strategic insights gained at NVIDIA’s GTC 2025, where AI’s convergence with simulation, synthetic data, and robotics signals a fundamental shift. Drawing from over 1,100 sessions and personal experiences at the heart of Silicon Valley, I explore emerging patterns that redefine what it means to build and deploy AI at scale. We’ll look beyond the hype of large language models to examine autonomous systems, interdisciplinary development, and the infrastructure shifts enabling AI everywhere—from cloud to desktop. This session is a call to technical leaders and practitioners to broaden their perspective, think beyond tools, and engage strategically. Whether you’re developing agents, managing data pipelines, or scaling AI across teams, this talk will challenge assumptions and highlight what truly matters in 2025 and beyond.</p>
|
|
| /talks/PLMJZ8/ |
<h3 class="talk-title">
Code & Community: The Synergy of Community Building and Task Automation
</h3>
<h4>
Cosima Meyer
</h4>
<div class="track">Natural Language Processing & Audio (incl. Generative AI NLP)</div>
<p class="talk-abstract">The Python community is built on a culture of support, inclusion, and collaboration. Sustaining this welcoming environment requires intentional community-building efforts, which often involve repetitive or time-consuming tasks. These tasks, however, can be automated without compromising their value—freeing up time for meaningful human engagement.
This talk showcases my project aimed at supporting underrepresented groups in tech, specifically through building Python communities on Mastodon and Bluesky. A key part of this initiative is the "Awesome PyLadies" repository, a curated collection of PyLadies blogs and YouTube channels that celebrates their work. To enhance visibility, I created a PyLadies bot for social media. This bot automates regular posts and reposts tagged content, significantly extending their reach and fostering an engaged community.
In this session, I’ll cover:
- The role of automation in community building
- The technical architecture behind the bot
- A hands-on demo on integrating Google’s Gemini into community tools
- Upcoming features and opportunities for collaboration
By combining Python, automation, and modern AI capabilities, we can create thriving, inclusive communities that scale impact while staying true to the human-centered ethos of open source.</p>
|
|
| /talks/DEHZHK/ |
<h3 class="talk-title">
Distributed file-systems made easy with Python's fsspec
</h3>
<h4>
Einat Orr, Barak Amar
</h4>
<div class="track">Data Handling & Engineering</div>
<p class="talk-abstract">The cloud native revolution has impacted all aspects of engineering, and data engineering is not exempt. One of the ongoing challenges in the data engineering world remains the local and distributed cloud native storage. In this talk we’ll explore working with distributed file systems in Python, through an intro to fsspec: a popular python library that is well-positioned to address the growing challenge of interacting with storage systems of different kinds in a consistent way.
In this talk we’ll show hands-on examples of working with fsspec with some of the most popular data tools in the Python community: Pandas, Tensorflow and PyArrow. We’ll demonstrate a real world implementation of fsspec and how it provides easy extensibility through open source tooling.
You’ll come away from this session with a better understanding for how to implement and extend fsspec to work with different cloud native storage systems.</p>
|
|
| /talks/ZT3MGL/ |
<h3 class="talk-title">
From Rules to Reality: Python's Role in Shaping Roundnet
</h3>
<h4>
Larissa Haas
</h4>
<div class="track">Data Handling & Engineering</div>
<p class="talk-abstract">Roundnet is a dynamic and fast-growing sport that combines quick reaction, athleticism, and strong community. However, like many emerging sports, it faces challenges in balancing competition, optimizing rules, and increasing accessibility for both players and spectators. This is where Python and data analysis come into play.
In this talk, I'll share insights from my role as Data Lead on the International Roundnet rule committee, where we use Python-powered data analysis to make informed decisions about the future of the sport. We'll explore how analyzing gameplay patterns and testing rule changes with simulation can lead to fairer, more exciting games and attract a broader audience.</p>
|
|
| /talks/RLTZTC/ |
<h3 class="talk-title">
How to use Data Science Superpowers in real life, a Bayesian perspective
</h3>
<h4>
Tim Lenzen
</h4>
<div class="track">Machine Learning & Deep Learning & Statistics</div>
<p class="talk-abstract">In the data science field, we use all these powerful methods to solve important problems. Most of the time, we do this very well because our data science and machine-learning toolbox fits the problems we tackle quite precisely. Yet, what about our everyday choices or even our most important life decisions? Can we use for our private lives what we advocate for in our jobs or are these choices inherently different?
Many of this real life decisions are a little different than textbook machine-learning problems. There is often less or hard-to-come-by data and the decisions are infrequent, but sometimes very consequential. This talk will dive into what makes everyday decisions difficult to handle with our data science toolbox. It will show how Bayesian thinking can help to reason in such cases, especially when there is not a lot of data to rely on.</p>
|
|
| /talks/CP3TKB/ |
<h3 class="talk-title">
Power up your Polars code with Polars extention
</h3>
<h4>
Cheuk Ting Ho
</h4>
<div class="track">Data Handling & Engineering</div>
<p class="talk-abstract">While Polars is written in Rust and has the advantages of speed and multi-threaded functionalities., everything will slow down if a Python function needs to be applied to the DataFrame. To avoid that, a Polar extension can be used to solve the problem. In this workshop, we will look at how to do it.</p>
|
|
| /talks/WLZSEZ/ |
<h3 class="talk-title">
A11y Need Is Love (But Accessible Docs Help Too)
</h3>
<h4>
Smera Goel
</h4>
<div class="track">PyData & Scientific Libraries Stack</div>
<p class="talk-abstract">Accessible documentation benefits everyone, from developers to end users. Using the [PyData Sphinx Theme](https://pydata-sphinx-theme.readthedocs.io/en/stable/) as a case study, this talk dives into common accessibility barriers in documentation websites like low contrast colors, missing focus states, etc. and practical ways to address them. Learn about accessibility improvements and take part in a live accessibility audit to see how small changes can make a big difference.</p>
|
|
| /talks/NBFH7G/ |
<h3 class="talk-title">
Streamlining the Cosmos: Pythonic Workflow Management for Astronomical Analysis
</h3>
<h4>
Raphael Hviding
</h4>
<div class="track">PyData & Scientific Libraries Stack</div>
<p class="talk-abstract">Astronomical surveys are growing rapidly in complexity and scale, necessitating accurate, efficient, and reproducible reduction and analysis pipelines. In this talk we explore Pythonic workflow managers to streamline processing large datasets on distributed computing environments.
Modern astronomy generates vast datasets across the electromagnetic spectrum. NASA's flagship James Webb Space Telescope (JWST) provides unprecedented observations that enable deep studies of distant galaxies, cosmic structures, and other astrophysical phenomena. However, these datasets are complex and require intricate calibration and analysis pipelines to transform raw data into meaningful scientific insights.
We will discuss the development and deployment of Pythonic tools, including snakemake and pixi, to construct modular, parallelized workflows for data reduction and analysis. Attendees will learn how these tools automate complex processing steps, optimize performance in distributed computing environments, and ensure reproducibility. Using real-world examples, we will illustrate how these workflows simplify the journey from raw data to actionable scientific insights.</p>
|
|
| /talks/WJPEQH/ |
<h3 class="talk-title">
The Mighty Dot - Customize Attribute Access with Descriptors
</h3>
<h4>
Mike Müller
</h4>
<div class="track">Python Language & Ecosystem</div>
<p class="talk-abstract">Whenever you use a dot after an object in Python you access an attribute. While this seems a very simple operation, behind the scenes many things can happen. This tutorial looks into this mechanism that is regulated by descriptors. You will learn how a descriptor works and what kind of problems it can help to solve. Python properties are based on descriptors and solve one type of problems. Descriptors are more general, allow more use cases, and are more re-usable. Descriptors are an advanced topic. But once mastered, they provide a powerful tool to hide potentially complex behavior behind a simple dot.</p>
|
|
| /talks/JUAF3S/ |
<h3 class="talk-title">
Challenges and Lessons Learned While Building a Real-Time Lakehouse using Apache Iceberg and Kafka
</h3>
<h4>
Jonas Böer, Elena Ouro Paz
</h4>
<div class="track">Data Handling & Engineering</div>
<p class="talk-abstract">How do you build a large-scale data lakehouse architecture that makes data available for business analytics in real time, while being more cost-effective, more flexible and faster than the previous proprietary solution? With Python, Kafka and Iceberg, of course!
We built a large-scale data lakehouse based on Apache Iceberg for the Schwarz Group, Europe's largest retailer. The system collects business data from thousands of stores, warehouses and offices across Europe.
In this talk, we will present our architecture, the challenges we faced, and how Apache Iceberg is shaping up to be the data lakehouse format of the future.</p>
|
|
| /talks/SXRVNU/ |
<h3 class="talk-title">
Data as (Python) Code
</h3>
<h4>
Francesco Calcavecchia
</h4>
<div class="track">MLOps & DevOps</div>
<p class="talk-abstract">In contemporary data-driven environments, the seamless integration of data into automated workflows is paramount. The reliability of automation, however, is constantly threatened by breaking changes in the source data. The Data-as-Code (DaC) paradigm address this challenge by treating data as a first-class citizen within the software development lifecycle.</p>
|
|
| /talks/ZACM3E/ |
<h3 class="talk-title">
Design, Generate, Deploy: Contract-First with FastAPI
</h3>
<h4>
Dr. Evelyne Groen, Kateryna Budzyak
</h4>
<div class="track">MLOps & DevOps</div>
<p class="talk-abstract">This talk explores a contract-first approach to API development using the OpenAPI generator, a powerful tool for automating API generation from a standardized specification. We will cover (1) what would you need to run to have a standard implementation of the FastAPI endpoints and data models; (2) how to customize the mustache templates that are used to generate the API stubs; (3) share some ideas how to customize the CLI and (4) how to maintain the contract and how to handle breaking changes to the contract. We will close the session with a discussion of the challenges of implementing the OpenAPI generator.</p>
|
|
| /talks/ZKNTGN/ |
<h3 class="talk-title">
expectation: A modern take on statistical A/B testing with e-values and martingales
</h3>
<h4>
Jako Rostami
</h4>
<div class="track">Machine Learning & Deep Learning & Statistics</div>
<p class="talk-abstract">This talk introduces a novel Python library for statistical testing using e-values, offering a refreshing alternative to traditional p-values. We'll explore how this approach enables real-time sequential testing, allowing data scientists to monitor experiments continuously without the statistical penalties of repeated testing. Through practical examples, we'll demonstrate how e-values provide more intuitive evidence measures and enable flexible stopping rules in A/B testing, clinical trials, and anomaly detection. The library implements cutting-edge methods from game-theoretic probability, making advanced sequential testing accessible to Python practitioners. Whether you're conducting A/B tests, monitoring production models, or running clinical trials, this talk will equip you with powerful new tools for sequential data analysis.</p>
|
|
| /talks/EDJ8N7/ |
<h3 class="talk-title">
Learnings from migrating a Flask app to FastAPI
</h3>
<h4>
Orell Garten
</h4>
<div class="track">Django & Web</div>
<p class="talk-abstract">FastAPI has been constantly growing in popularity during the last years. A lot of this growth is driven by its relative simplicity and ease-of-use. In this talk, we'll discuss some practical insights into building a FastAPI application, based on my experience of migrating an existing Flask prototype to FastAPI.
We'll explore how FastAPI's core features like Pydantic integration and dependency injection can improve API development, while also talking about the drawbacks of FastAPI.</p>
|
|
| /talks/MRHNCV/ |
<h3 class="talk-title">
Open Table Formats in the Wild: From Parquet to Delta Lake and Back
</h3>
<h4>
Franz Wöllert
</h4>
<div class="track">Data Handling & Engineering</div>
<p class="talk-abstract">Open table formats have revolutionized analytical, columnar storage on cloud object stores with critical features like ACID compliance and enhanced metadata management, once exclusive to proprietary cloud data warehouses. Delta Lake, Iceberg, and Hudi have significantly advanced over traditional open file formats like Parquet and ORC.
In an effort to modernize our data architecture, we aimed to replace our Parquet-based bronze layer with Delta Lake, anticipating better query performance, reduced maintenance, native support for incremental processing, and more. While our initial pilot showed promise, we encountered unexpected pitfalls that ultimately brought us back to where we began.
Curious? Join me as we shed light on the current state of table formats.</p>
|
|
| /talks/B8TUR9/ |
<h3 class="talk-title">
Optimizing Energy Tariffing System with Formal Concept Analysis and Dash
</h3>
<h4>
Dr. Irina Smirnova-Pinchukova
</h4>
<div class="track">Visualisation & Jupyter</div>
<p class="talk-abstract">As a data scientist, I value the power of insightful visualizations to unlock unique interpretations of complex data. In my talk, I will introduce an elegant mathematical framework called Formal Concept Analysis (FCA), developed in the 1980s in Darmstadt.
FCA transforms binary data into concepts that can be visualized as a hierarchical graph, offering a fresh perspective on multidimensional data analysis. Leveraging this theory and its open-source Python libraries, I am developing an interactive Dash-based tool featuring interactive tables and graphs to explore data insights.
To illustrate its potential, I will showcase an optimization of the entire tariffing system of an energy provider company, highlighting how FCA can bring structure and clarity to even such tangled datasets.</p>
|
|
| /talks/BLKYGU/ |
<h3 class="talk-title">
Streamlining Python deployment with Pixi: A Perspective from production
</h3>
<h4>
Dennis Weyland
</h4>
<div class="track">MLOps & DevOps</div>
<p class="talk-abstract">In our quest to improve Python deployments, we explored Pixi, a tool designed to enhance dependency management within the Conda ecosystem. This talk recounts our experience integrating Pixi into a setup used in production. We leveraged Pixi to create lockfiles, ensuring consistent builds, and to automate deployments via CI/CD pipelines. This integration led to greater reliability and efficiency, minimizing deployment errors and allowing us to concentrate more on development. Join us as we share how Pixi transformed our deployment process and offer insights into optimizing your own workflows.</p>
|
|
| /talks/N9CAUM/ |
<h3 class="talk-title">
supplyseer: Computational Supply Chain with Python
</h3>
<h4>
Jako Rostami
</h4>
<div class="track">Machine Learning & Deep Learning & Statistics</div>
<p class="talk-abstract">This talk introduces supplyseer, an open-source Python library that brings advanced analytics to Supply Chain and Logistics. By combining time series embedding techniques, stochastic process modeling, and geopolitical risk analysis, supplyseer helps organizations make data-driven decisions in an increasingly complex global supply chain landscape. The library implements novel approaches like Takens embedding for demand forecasting, Hawkes processes for modeling supply chain events, and Bayesian methods for inventory optimization. Through practical examples and real-world use cases, we'll explore how these mathematical concepts translate into actionable insights for supply chain practitioners.</p>
|
|
| /talks/LBKU3T/ |
<h3 class="talk-title">
What's inside the box? Building a deep learning framework from scratch.
</h3>
<h4>
Oleh Kostromin
</h4>
<div class="track">Machine Learning & Deep Learning & Statistics</div>
<p class="talk-abstract">Explore the inner workings of deep learning frameworks like TensorFlow and PyTorch by building your own in this workshop. We will start with the fundamental automatic differentiation mechanics and proceed to implementing more complex components like layers, modules and optimizers. This workshop is mainly designed for experienced data scientists, who want to expand their intuition about lower level framework internals.</p>
|
|
| /talks/TQN98D/ |
<h3 class="talk-title">
Algorithmic Music Composition With Python
</h3>
<h4>
Hendrik Niemeyer
</h4>
<div class="track">Python Language & Ecosystem</div>
<p class="talk-abstract">Computers have long been an integral part of creating music. Virtual instruments and digital audio workstations make creating music easy and accessible. But how do programming languages and especially Python fit into this? Python can serve as a tool for creating musical notation
and MIDI files.
Throughout the session, you’ll learn how to:
- Use Python to create melodies, harmonies, and rhythms.
- Generate music based on rules, randomness, and mathematical principles.
- Visualize and export your compositions as MIDI and sheet music.
By the end of the talk, you’ll have a clear understanding of how to turn simple algorithms into expressive musical works.</p>
|
|
| /talks/TXKLWR/ |
<h3 class="talk-title">
Analyze data easily with duckdb - and the implications on data architectures
</h3>
<h4>
Matthias Niehoff
</h4>
<div class="track">Data Handling & Engineering</div>
<p class="talk-abstract">duckdb is increasingly becoming a universal tool for accessing and analyzing data. In this talk I will show with slides and live demo what duckdb is capable of and will dive deeper in how it will influence modern data architectures.</p>
|
|
| /talks/HYE8EX/ |
<h3 class="talk-title">
Autonomous Browsing using Large Action Models
</h3>
<h4>
Nico Kreiling, Arne Grobrügge
</h4>
<div class="track">Natural Language Processing & Audio (incl. Generative AI NLP)</div>
<p class="talk-abstract">The browser serves as our gateway to the internet—the largest repository of knowledge in human history. Proficiency in its use is a core skill across nearly all professions and is becoming increasingly important for Artificial Intelligence. But can Large Action Models (LAMs) autonomously operate a browser? What exactly are LAMs that promise to translate human intentions into actions? We report on a project that fully automates the job application process using AI: from navigating unfamiliar website structures and filling out forms to handling document uploads and cookie banners.</p>
|
|
| /talks/ER3V7W/ |
<h3 class="talk-title">
Bias Meets Bayes: A Bayesian Perspective on Improving Model Fairness
</h3>
<h4>
Vince Nelidov
</h4>
<div class="track">Machine Learning & Deep Learning & Statistics</div>
<p class="talk-abstract">Bias in machine learning models remains a pressing issue, often disproportionately affecting the most vulnerable groups in society. This talk introduces a Bayesian perspective to effectively tackle these challenges, focusing on improving fairness by modeling and addressing bias directly.
You will learn about the interplay between uncertainty, equity, and predictive accuracy, while gaining actionable insights to improve fairness in diverse applications. Using a practical example of a risk-scoring model trained on data with underrepresented minority groups, I will showcase how Bayesian methods compare to traditional techniques, demonstrating their unique potential to mitigate bias while maintaining performance.</p>
|
|
| /talks/NF8UPF/ |
<h3 class="talk-title">
Building an Open Source RAG System for the United Nations Negotiations on Global Plastic Pollution
</h3>
<h4>
Rahkakavee Baskaran, Teresa Kroesen, Anna-Lisa Wirth
</h4>
<div class="track">Natural Language Processing & Audio (incl. Generative AI NLP)</div>
<p class="talk-abstract">Plastic pollution is a significant global challenge. Every year, millions of tons of plastic enter the oceans, impacting marine ecosystems and human health. To address this issue, the United Nations is negotiating a legally binding treaty with representatives from 180 countries, aiming to reduce plastic pollution and promote sustainable practices.
We have developed NegotiateAI, an open-source chat application that supports delegations during the UN negotiations on a legally binding agreement to combat plastic pollution. The tool demonstrates how generative AI and Retrieval Augmented Systems (RAG) can address complex global challenges. Built with Haystack 2.0, Qdrant, HuggingFace Spaces, and Streamlit, it showcases the potential of open-source technologies in tackling issues of global relevance.
As a beginner or advanced developer, this talk will give you valuable insights into developing impactful AI applications with open source tools in the public sector.</p>
|
|
| /talks/M98YBR/ |
<h3 class="talk-title">
Enhancing Software Supply Chain Security with Open Source Python Tools
</h3>
<h4>
Anthony Harrison
</h4>
<div class="track">Security</div>
<p class="talk-abstract">The Cyber Resilience Act (CRA) is focused on improving the security and resilience of digital products. But to comply with the CRA, businesses will need to start preparing the necessary evidence to ensure compliance if they want to continue to deliver digital products to the EU market once the CRA is in force.
Key requirements within the CRA include implementing robust security measures throughout the product life-cycle, adopting secure development practices and implementing proactive vulnerability management processes.
This session will show how a number of the requirements for the CRA can be achieved by use of a number of open source Python tools.</p>
|
|
| /talks/FSK3PE/ |
<h3 class="talk-title">
From Queries to Confidence: Ensuring SQL Reliability with Python
</h3>
<h4>
Anna Varzina
</h4>
<div class="track">Testing</div>
<p class="talk-abstract">SQL remains a foundational component of data-driven applications, but ensuring the accuracy and reliability of SQL logic is often challenging. SQL testing can be cumbersome, time-consuming, and error-prone. However, these challenges can be addressed by leveraging the simplicity of Python's testing framework such as pytest, enabling clean, robust, and automated SQL testing.</p>
|
|
| /talks/3DSU8V/ |
<h3 class="talk-title">
Hands-On LLM Security: Attacks and Countermeasures You Need to Know!
</h3>
<h4>
Clemens Hübner, Florian Teutsch
</h4>
<div class="track">Security</div>
<p class="talk-abstract">Dive into the vulnerabilities of LLMs and learn how to prevent them
From prompt injection to data poisoning, we’ll demonstrate real-world attack scenarios and reveal essential countermeasures to safeguard your applications.</p>
|
|
| /talks/CPCNRZ/ |
<h3 class="talk-title">
How Narwhals is silently bringing pandas, Polars, DuckDB, PyArrow, and more together
</h3>
<h4>
Marco Gorelli
</h4>
<div class="track">PyData & Scientific Libraries Stack</div>
<p class="talk-abstract">If you were writing a data science tool in 2015, you'd have ensured it supported pandas and then called it a day.
But it's not 2015 anymore, we've fast-forwarded to 2025. If you write a tool which only supports pandas, users will demand support for Polars, PyArrow, DuckDB, and so many other libraries that you'll feel like giving up.
Learn about how Narwhals allows you to write dataframe-agnostic tools which can support all of the above, with zero dependencies, low overhead, static typing, and strong backwards-compatibility promises!</p>
|
|
| /talks/GURXPK/ |
<h3 class="talk-title">
Is Prompt Engineering Dead? How Auto-Optimization is Changing the Game
</h3>
<h4>
Iryna Kondrashchenko, Oleh Kostromin
</h4>
<div class="track">Natural Language Processing & Audio (incl. Generative AI NLP)</div>
<p class="talk-abstract">The rise of LLMs has elevated prompt engineering as a critical skill in the AI industry, but manual prompt tuning is often inefficient and model-specific. This talk explores various automatic prompt optimization approaches, ranging from simple ones like bootstrapped few-shot to more complex techniques such as MIPRO and TextGrad, and showcases their practical applications through frameworks like DSPy and AdalFlow. By exploring the benefits, challenges, and trade-offs of these approaches, the attendees will be able to answer the question: is prompt engineering dead, or has it just evolved?</p>
|
|
| /talks/XLZQFA/ |
<h3 class="talk-title">
Lessons learned in bringing a RAG chatbot with access to 50k+ diverse documents to production
</h3>
<h4>
Bernhard Schäfer, Nico Mohr
</h4>
<div class="track">Generative AI</div>
<p class="talk-abstract">Retrieval-Augmented Generation (RAG) chatbots are a key use case of GenAI in organizations, allowing users to conveniently access and query internal company data. A first RAG prototype can often be created in a matter of days. But why are the majority of prototypes still in the pilot stage? [\[1\]](https://www2.deloitte.com/content/dam/Deloitte/us/Documents/consulting/us-state-of-gen-ai-q3.pdf)
In this talk we share our insights from developing a production-grade chatbot at Merck. Our RAG chatbot for R&D experts accesses over 50,000 documents across numerous SharePoint sites and other sources. We identified three technical key success factors:
1. Building a robust data pipeline that syncs documents from source systems and that handles enterprise features such as replicating user permissions.
2. Developing a chatbot workflow from user question to answer with retrieval components such as hybrid search and reranking
3. Establishing a comprehensive evaluation framework with a clear optimization metric.
We think that many of these lessons are broadly applicable to RAG chatbots, making this talk valuable for practitioners aiming to implement GenAI solutions in business contexts.</p>
|
|
| /talks/F9EFXA/ |
<h3 class="talk-title">
Modern NLP for Proactive Harmful Content Moderation
</h3>
<h4>
Daryna Dementieva
</h4>
<div class="track">Natural Language Processing & Audio (incl. Generative AI NLP)</div>
<p class="talk-abstract">Despite an array of regulations implemented by governments and social media platforms worldwide (i.e. famous DSA), the problem of digital abusive speech persists. At the same time, rapid advances in NLP and large language models (LLMs) are opening up new possibilities—and responsibilities—for using this technology to make a positive social impact. Can LLMs streamline content moderation efforts? Are they effective at spotting and countering hate speech, and can they help produce more proactive solutions like text detoxification and counter-speech generation?
In this talk, we will dive into the cutting-edge research and best practices of automatic textual content moderation today. From clarifying core definitions to detailing actionable methods for leveraging multilingual NLP models, we will provide a practical roadmap for researchers, developers, and policymakers aiming to tackle the challenges of harmful online content. Join us to discover how modern NLP can foster safer, more inclusive digital communities.</p>
|
|
| /talks/3WLDMQ/ |
<h3 class="talk-title">
Navigating the Security Maze: An Interactive Adventure
</h3>
<h4>
Clemens Hübner
</h4>
<div class="track">Security</div>
<p class="talk-abstract">How to integrate security into a software development project? Without jeopardizing timeline or budget? You decide!
This interactive session covers crucial decisions for software security, and the audience decides how the story ends...</p>
|
|
| /talks/HPGEKH/ |
<h3 class="talk-title">
Outgrowing your node? Zero stress scaling with cuPyNumeric.
</h3>
<h4>
Bo Dong
</h4>
<div class="track">Programming & Software Engineering</div>
<p class="talk-abstract">Many data and simulation scientists use NumPy for its ease of use and good performance on CPU. This approach works well for single-node tasks, but scaling to handle larger datasets or more resource-intensive computations introduces significant challenges. Not to mention, using GPUs requires another level of complexity. We present the cuPyNumeric library, which gives developers the same familiar NumPy interface, but seamlessly distributes work across CPUs and GPUs.
In this talk we showcase the productivity and performance of cuPyNumeric library on one of the user's examples covering some detail on its implementation.</p>
|
|
| /talks/JH97CL/ |
<h3 class="talk-title">
Pipeline-level differentiable programming for the real world
</h3>
<h4>
Alessandro Angioi
</h4>
<div class="track">Research Software Engineering</div>
<p class="talk-abstract">Automatic Differentiation (AD) is not only the backbone of modern deep learning but also a transformative tool across various domains such as control systems, materials science, weather prediction, 3D rendering, data-driven scientific discovery, and so on. Thanks to a mature ML framework ecosystem, powered by libraries like PyTorch and JAX, AD performs remarkably well at a component level; however, integrating these components into differentiable pipelines still remains a significant challenge. In this talk, we will provide an accessible introduction to (pipeline-level) AD, demonstrate some cool applications you can build with it, and see how to build differentiable pipelines that hold up in the real world.</p>
|
|
| /talks/AEUZGX/ |
<h3 class="talk-title">
PosePIE: Replace Your Keyboard and Mouse With AI-Driven Gesture Control
</h3>
<h4>
Daniel Stolpmann
</h4>
<div class="track">Computer Vision (incl. Generative AI CV)</div>
<p class="talk-abstract">In this talk, we show how to leverage publicly available tools to control any game or program using hand or body movements. To achieve this, we introduce PosePIE, an open-source programmable input emulator that generates input events on virtual gamepads, keyboards and mice based on gestures recognized by using AI-driven pose estimation. PosePIE is fully configurable by the user through Python scripts, making it easily adaptable to new applications.</p>
|
|
| /talks/TQLGA8/ |
<h3 class="talk-title">
Reinforcement Learning Without a PhD: A Python Developer’s Journey
</h3>
<h4>
Jochen Luithardt
</h4>
<div class="track">Machine Learning & Deep Learning & Statistics</div>
<p class="talk-abstract">Reinforcement Learning (RL) has shown superhuman performance in games and is already delivering value in Big Tech. But despite its potential, RL remains largely inaccessible to most developers. Why? Because real-world RL is hard—it demands data, infrastructure, and tools that are often built for researchers, not practitioners.
This talk shares the journey of applying RL to a real-world use case without having a PhD. It’s a story of figuring things out through hands-on experimentation, trial and error, and building what didn’t exist. We’ll explore what makes RL powerful, why it’s still rare in practice, and how you can get started. Along the way, you’ll learn about the key challenges of production RL, how to work around them, and how the open-source toolkit pi_optimal can help bridge the gap. Whether you're just RL-curious or ready to dive in, this talk offers practical insights and a demo to help you take your first steps.</p>
|
|
| /talks/TRUUVL/ |
<h3 class="talk-title">
Scaling Python: An End-to-End ML Pipeline for ISS Anomaly Detection with Kubeflow
</h3>
<h4>
Christian Geier, Henrik Sebastian Steude
</h4>
<div class="track">MLOps & DevOps</div>
<p class="talk-abstract">Building and deploying scalable, reproducible machine learning pipelines can be challenging, especially when working with orchestration tools like Slurm or Kubernetes. In this talk, we demonstrate how to create an end-to-end ML pipeline for anomaly detection in International Space Station (ISS) telemetry data using only Python code.
We show how Kubeflow Pipelines, MLFlow, and other open-source tools enable the seamless orchestration of critical steps: distributed preprocessing with Dask, hyperparameter optimization with Katib, distributed training with PyTorch Operator, experiment tracking and monitoring with MLFlow, and scalable model serving with KServe. All these steps are integrated into a holistic Kubeflow pipeline.
By leveraging Kubeflow's Python SDK, we simplify the complexities of Kubernetes configurations while achieving scalable, maintainable, and reproducible pipelines. This session provides practical insights, real-world challenges, and best practices, demonstrating how Python-first workflows empower data scientists to focus on machine learning development rather than infrastructure.</p>
|
|
| /talks/GRWYQB/ |
<h3 class="talk-title">
Securing RAG Pipelines with Fine Grained Authorization
</h3>
<h4>
Sohan Maheshwar
</h4>
<div class="track">Generative AI</div>
<p class="talk-abstract">Using LLMs and AI in your Enterprise? Make sure you build Fine Grained Authorization to ensure your LLMs access only the data they are authorized to.
This talk will show how you can build Relationship Based Access Control (ReBAC) for fine-grained authorization for your RAG pipelines. The talk also includes a demo using Pinecone, Langchain, OpenAI, and SpiceDB.</p>
|
|
| /talks/VDG9YG/ |
<h3 class="talk-title">
Taking Control of LLM Outputs: An Introductory Journey into Logits
</h3>
<h4>
Emek Gözlüklü
</h4>
<div class="track">Natural Language Processing & Audio (incl. Generative AI NLP)</div>
<p class="talk-abstract">This talk explores logits - the raw confidence scores that language models generate before selecting each token. Understanding and manipulating these scores gives you practical control over how models generate text.
In this introductory session, we'll explore the token-by-token generation process, examining how tokenizers work and why vocabulary matters. You'll learn about the relationship between logits, probabilities, and tokens. Then we will cover constrained decoding approaches and talk about structured generation.</p>
|
|
| /talks/XRHEYZ/ |
<h3 class="talk-title">
The Foundation Model Revolution for Tabular Data
</h3>
<h4>
Noah Hollmann, Frank Hutter
</h4>
<div class="track">Machine Learning & Deep Learning & Statistics</div>
<p class="talk-abstract">What if we could make the same revolutionary leap for tables that ChatGPT made for text? While foundation models have transformed how we work with text and images, tabular / structured data (spreadsheets and databases) - the backbone of economic and scientific analysis - has been left behind. TabPFN changes this. It's a foundation model that achieves in 2.8 seconds what traditional methods need 4 hours of hyperparameter tuning for - while delivering better results. On datasets up to 10,000 samples, it outperforms every existing Python library, from XGBoost to CatBoost to Autogluon.
Beyond raw performance, TabPFN brings foundation model capabilities to tables: native handling of messy data without preprocessing, built-in uncertainty estimation, synthetic data generation, and transfer learning - all in a few lines of Python code. Whether you're building risk models, accelerating scientific research, or optimizing business decisions, TabPFN represents the next major transformation in how we analyze data. Join us to explore and learn how to leverage these new capabilities in your work.</p>
|
|
| /talks/MJD7TG/ |
<h3 class="talk-title">
What do a tree and the human brain have in common-a not so serious introduction to digital pathology
</h3>
<h4>
Daniel Hieber
</h4>
<div class="track">Computer Vision (incl. Generative AI CV)</div>
<p class="talk-abstract">While trees and human brains don't share that many properties regarding their domain, the analysis of the height of a tree and cancer in human brains does.
This talk provides a not-so-serious introduction to the domain of computer vision for pathological use cases.
Besides a general introduction to (digital) pathology and the technical similarities between satellite images (GeoTIFs) and pathological images (Whole-Slide Images), we will take a look at computer vision for medical tasks using Python.
Whether you have never done image processing in Python, are an expert (ready to share some tricks with me), or are just curious to see pictures of a human brain, this talk is for you.
Warning: this talk contains quite abstract pink-ish pictures of human tissue (and trees^^). If you are unsure this is something you are comfortable with (have a friend), do a quick search for "HE-stained whole-slide image".</p>
|
|
| /talks/ADSXCA/ |
<h3 class="talk-title">
Where have all the post offices gone? Discovering neighborhood facilities with Python and OSM
</h3>
<h4>
Katie Richardson
</h4>
<div class="track">Data Handling & Engineering</div>
<p class="talk-abstract">When it comes to open geographic data, OpenStreetMap is an awesome resource. Getting started and figuring out how to make the most out of the data available can be challenging.
Using a personal example: frustration at the apparent lack of post offices in my neighborhood, we'll walk through examples of how to parse, filter, process, and visualize geospatial data with Python.
At the end of this talk, you will know how to process geographic data from OpenStreetMap using Python and find out some surprising info that I learned while answering the question: Where have all the post offices gone?</p>
|
|
| /talks/ZMKJAY/ |
<h3 class="talk-title">
Death by a Thousand API Versions
</h3>
<h4>
Stanislav Zmiev
</h4>
<div class="track">Django & Web</div>
<p class="talk-abstract">API versioning is tough, really tough. We tried multiple approaches to versioning in production and eventually ended up with a solution we love. During this talk you will look into the tradeoffs of the most popular ways to do API versioning, and I will recommend which ones are fit for which products and companies. I will also present my framework, Cadwyn, that allows you to support hundreds of API versions with ease -- based on FastAPI and inspired by Stripe's approach to API versioning.
After this session, you will understand which approach to pick for your company to make your versioning cost effective and maintainable without investing too much into it.</p>
|
|
| /talks/JABVHK/ |
<h3 class="talk-title">
Enhancing RAG with Fast GraphRAG and InstructLab: A Scalable, Interpretable, and Efficient Framework
</h3>
<h4>
Tuhin Sharma
</h4>
<div class="track">Generative AI</div>
<p class="talk-abstract">Retrieval Augmented Generation (RAG) has become a cornerstone in enriching GenAI outputs with external data, yet traditional frameworks struggle with challenges like data noise, domain specialization, and scalability. In this talk, Tuhin will dive into open-source frameworks Fast GraphRAG and InstructLab, which addresses these limitations by combining knowledge graphs with the classical PageRank algorithm and Fine-tuning, delivering a precision-focused, scalable, and interpretable solution. By leveraging the structured context of knowledge graphs, Fast GraphRAG enhances data adaptability, handles dynamic datasets efficiently, and provides traceable, explainable outputs while InstructLab adds domain depth to the LLM through Fine-tuning. Designed for real-world applications, it bridges the gap between raw data and actionable insights, redefining intelligent retrieval for developers, researchers, and enterprises. This talk will showcase Fast GraphRAG’s transformative features coupled with domain specific Fine-tuning leveraging InstructLab and demonstrate its potential to elevate RAG’s capabilities in handling the evolving demands of large language models (LLMs) for developers, researchers, and businesses.</p>
|
|
| /talks/P9VKRV/ |
<h3 class="talk-title">
Extending Python with Rust, Mojo, Cuda and C and building packages
</h3>
<h4>
Ruben Arts, Wolf Vollprecht
</h4>
<div class="track">PyData & Scientific Libraries Stack</div>
<p class="talk-abstract">We all love Python - but we especially love it for its unique ability as a glue language.
In this talk we will show a number of ways of extending Python: using Rust, C and Cython, C++, CUDA and Mojo! We will use the pixi package manager and the open source conda-forge distribution to demonstrate how to easily build custom Python extensions with these languages.
The main challenge with custom extensions is about distributing them. The new pixi build feature makes it easy to build a Python extension into a conda package as well as wheel file for PyPI.
Pixi will manage not only Python, but also the compilers and other system-level dependencies.</p>
|
|
| /talks/83QH37/ |
<h3 class="talk-title">
From Trees to Transformers: Our Journey Towards Deep Learning for Ranking
</h3>
<h4>
Theodore Meynard, Mihail Douhaniaris
</h4>
<div class="track">Machine Learning & Deep Learning & Statistics</div>
<p class="talk-abstract">GetYourGuide, a global marketplace for travel experiences, reached diminishing returns with its XGBoost-based ranking system. We switched to a Deep Learning pipeline in just nine months, maintaining high throughput and low latency. We iterated on over 50 offline models and conducted more than 10 live A/B tests, ultimately deploying a PyTorch transformer that yielded significant gains. In this talk, we will share our phased approach—from a simple baseline to a high-impact launch—and discuss the key operational and modeling challenges we faced. Learn how to transition from tree-based methods to neural networks and unlock new possibilities for real-time ranking.</p>
|
|
| /talks/U9KHNA/ |
<h3 class="talk-title">
Getting Started with Bayes in Engineering: Implementing Kalman Filters with RxInfer.jl
</h3>
<h4>
Victor Flores Terrazas
</h4>
<div class="track">Research Software Engineering</div>
<p class="talk-abstract">Bayesian methods are not commonly seen in Civil Engineering and Structural Dynamics. In this talk we explore how RxInfer.jl and the Julia Programming Language can simplify Bayesian modeling by implementing a Kalman filter for tracking the dynamics of a structural system. Perfect for engineers, researchers, and data scientists eager to apply probabilistic modelling and Bayesian methods to real-world engineering challenges.</p>
|
|
| /talks/RQ8JBM/ |
<h3 class="talk-title">
Intuitive A/B Test Evaluations for Coders
</h3>
<h4>
Thomas Mayer
</h4>
<div class="track">Machine Learning & Deep Learning & Statistics</div>
<p class="talk-abstract">A/B testing is a critical tool for making data-driven decisions, yet its statistical underpinnings—p-values, confidence intervals, and hypothesis testing—are often challenging for those without a background in statistics. Coders frequently encounter these concepts but lack a straightforward way to compute and interpret them using their existing skill set.
This talk presents a practical approach to A/B test evaluations tailored for coders. By utilizing Python’s random number generator and basic loops, it introduces bootstrapping as an accessible method for calculating p-values and confidence intervals directly from data. The goal is to simplify statistical concepts and provide coders with an intuitive understanding of how to evaluate test results without relying on complex formulas or statistical jargon.</p>
|
|
| /talks/NNGWGC/ |
<h3 class="talk-title">
Mastering Demand Forecasting: Lessons from Europe's Largest Retailer
</h3>
<h4>
Moreno Schlageter, Yovli Duvshani
</h4>
<div class="track">Machine Learning & Deep Learning & Statistics</div>
<p class="talk-abstract">Ever craved your favorite dish, only to find its key ingredient missing from the store? You're not alone - stock outs can have significant consequences for businesses, resulting in frustrated customers and lost sales. On the other hand, overstocking can lead to wasted storage costs and potential write-offs. The replenishment system is responsible for striking the right balance between these opposing risks.
The key to successful replenishment is making accurate predictions about future demand.
This presentation takes a deep dive into the intricate world of demand forecasting, at Europe's largest retailer. We will demonstrate how enhancing simple machine learning methods with domain knowledge allows to generate hundreds of millions of high-quality forecasts every day.</p>
|
|
| /talks/ME7XPJ/ |
<h3 class="talk-title">
Oh my license! – Achieving order by automation in the license chaos of your dependencies
</h3>
<h4>
Paul Müller
</h4>
<div class="track">Programming & Software Engineering</div>
<p class="talk-abstract">License issues can haunt you at night.
You spend days, weeks, and months developing beautiful software.
But then it happens.
You realize that an essential dependency is GPL-3.0 licensed.
All your code is now infected with this license.
Now you are forced to either:
1. Rewrite all parts relying on the other library
2. Open-source your codebase under the GPL-3.0 license
How could this have been avoided?
Join the talk and find out!
First, we’ll give you a brief introduction to different software licenses and their implications.
Second, we’ll show you how to automate your license checking using open-source software.</p>
|
|
| /talks/VFE78U/ |
<h3 class="talk-title">
They are not unit tests: a survey of unit-testing anti-patterns
</h3>
<h4>
Stanislav Zmiev
</h4>
<div class="track">Testing</div>
<p class="talk-abstract">The entire industry approves of unit testing but almost no one can fully agree on how to do it correctly, or even on what unit tests are. This results in unit tests often being associated with slower development cycle and an overall less enjoyable workflow. I'll show you how testing turns into hell in real enterprises with the most common anti-patterns and then I'll show you that most of them are avoidable with modern tooling like mutation testing, snapshot testing, dirty-equals, and many more. We'll discuss how to make tests speed up your development and make refactoring easy.</p>
|
|
| /talks/89BX8V/ |
<h3 class="talk-title">
Towards Intelligent Monitoring: Detecting Degraded Flame Torch Nozzles
</h3>
<h4>
Dominik Falkner
</h4>
<div class="track">Machine Learning & Deep Learning & Statistics</div>
<p class="talk-abstract">Flame cutting is a method where metals are efficiently cut using precise control of the oxygen jet and consistent mixing of fuel gas. The condition of the nozzle is changing over time: deposits formed during the cutting process can degrade the flame quality, reducing the precision of the cut. Traditionally, nozzles suspected of wear are sent back for manual inspection, where experts evaluated the flame visually and audibly to determine whether repair or replacement is needed. This project leverages machine learning to optimize this process by analyzing acoustic emission data.</p>
|
|
| /talks/GUKTNX/ |
<h3 class="talk-title">
Benchmarking Time Series Foundation Models with sktime
</h3>
<h4>
Benedikt Heidrich, Franz Kiraly
</h4>
<div class="track">Machine Learning & Deep Learning & Statistics</div>
<p class="talk-abstract">Recent time series foundation models such as LagLlama, Chronos, Moirai, and TinyTimesMixer promise zero-shot forecasting for arbitrary time series. One central claim of foundation models is their ability to perform zero-shot forecasting, that is, to perform well with no training data. However, performance claims of foundation models are difficult to verify, as public benchmark datasets may have been a part of the training data, and only the already trained weights are available to the user.
Therefore, performance in specific use cases must be verified based on the use case data itself to ensure a reliable assessment of forecasting performance. sktime allows users to easily produce a performance benchmark of any collection of forecasting models, foundation models, simple baselines, or custom methods on their internal use case data.</p>
|
|
| /talks/DQTMJB/ |
<h3 class="talk-title">
Beyond Basic Prompting: Supercharging Open Source LLMs with LMQL's Structured Generation
</h3>
<h4>
Christiaan Swart
</h4>
<div class="track">Natural Language Processing & Audio (incl. Generative AI NLP)</div>
<p class="talk-abstract">This intermediate-level talk demonstrates how to leverage Language Model Query Language (LMQL) for structured generation and tool usage with open-source models like Llama. You will learn how to build a RAG system that enforces output constraints, handles tool calls, and maintains response structure - all while using open-source components. The presentation includes hands-on examples where audience members can experiment with LMQL prompts, showcasing real-world applications of constrained generation in production environments.</p>
|
|
| /talks/LRUKZQ/ |
<h3 class="talk-title">
Beyond DALL-E: Advanced Image Generation Workflows with ComfyUI
</h3>
<h4>
René Fa
</h4>
<div class="track">Computer Vision (incl. Generative AI CV)</div>
<p class="talk-abstract">Image generation using AI has made huge progress over the last years, and many people still think that DALL-E with a text prompt is the best way to generate images. There are well-known models like Stable Diffusion and Flux, which can be used with easy-to-use frontends like A1111 or Invoke AI, but if you want to do more complex or bleeding-edge workflows, you need something else. In this talk, I want to show you ComfyUI, an open-source node-based GUI written in Python where you can build complex pipelines that are otherwise only possible using plain code.</p>
|
|
| /talks/MSUCAS/ |
<h3 class="talk-title">
Beyond FOMO — Keeping Up-to-Date in AI
</h3>
<h4>
Carsten Frommhold
</h4>
<div class="track">Education, Career & Life</div>
<p class="talk-abstract">The rapid evolution of AI technologies, particularly since the emergence of Large Language Models, has transformed the data science landscape from a field of steady progress to one of constant breakthroughs. This acceleration creates unique challenges for practitioners, from managing FOMO to battling imposter syndrome. Drawing from personal experience transitioning from mathematical modeling to modern AI development, this talk explores practical strategies for staying current while maintaining sanity. We'll discuss building effective learning structures, creating collaborative knowledge-sharing environments, and finding the right balance between innovation and implementation. Attendees will leave with actionable insights on navigating technological change while fostering sustainable growth in their teams and careers.</p>
|
|
| /talks/7CL3KS/ |
<h3 class="talk-title">
Bridging the gap: unlocking SAP data for data lakes with Python and PySpark via SAP Datasphere
</h3>
<h4>
Rostislaw Krassow
</h4>
<div class="track">Data Handling & Engineering</div>
<p class="talk-abstract">SAP's data often remains locked away, hindering the creation of a complete data picture. This talk presents a hands-on proof of concept leveraging SAP Datasphere, Python and PySpark to bridge an Azure-based, data mesh-inspired open data lake with a centralized SAP BI environment.
This presentation will delve into the architecture of SAP Datasphere and its integration interfaces with Python. It will explore network integration, authentication, authorization and resource management options, as well as data integration patterns. The presentation will summarize the evaluated features and limitations discovered during the PoC.</p>
|
|
| /talks/3CYZUH/ |
<h3 class="talk-title">
Building a Self-Hosted MLOps Platform with Kubernetes
</h3>
<h4>
Josef Nagelschmidt
</h4>
<div class="track">MLOps & DevOps</div>
<p class="talk-abstract">Many managed MLOps platforms, while convenient, often fall short in providing flexibility, requiring complex integrations, and causing vendor lock-in. In this talk, we’ll share our experience transitioning from managed MLOps tools to a self-hosted solution built on Kubernetes. We’ll focus on how we leveraged open-source tools like Feast, MLflow, and Ray to build a more flexible, scalable, and customizable platform that is now in use at Rewe Digital. By migrating to this self-hosted architecture, we gained greater control over our ML pipelines, reduced our dependency on third-party services, and created a more adaptable infrastructure for our ML workloads.</p>
|
|
| /talks/VJR39N/ |
<h3 class="talk-title">
Citation is Collaboration: Software Recognition in Research and Industry
</h3>
<h4>
Ivelina Momcheva
</h4>
<div class="track">Research Software Engineering</div>
<p class="talk-abstract">The development of open source software is increasingly recognized as a critical contribution across many disciplines, yet the mechanisms for credit and citation vary significantly. This talk uses astronomy as a case study to explore shared challenges in attributing software contributions across research and industry. It will review the evolution of journal recommendations and policies over the past decade, alongside emerging publishing practices offering insights into their impact on the recognition of software contributions. An analysis of citation patterns for widely used libraries (numpy, scipy, astropy) highlights trends over time and their dependence on publication venues and policies. The talk will conclude with strategies for both developers and users for improving the recognition of software, fostering collaboration and sustainability in software ecosystems. All data and analysis code will be made available in a public repository, supporting transparency and further study.</p>
|
|
| /talks/J8FLDN/ |
<h3 class="talk-title">
Conquering the Queue: Lessons from processing one billion Celery tasks
</h3>
<h4>
Daniel Hepper
</h4>
<div class="track">Django & Web</div>
<p class="talk-abstract">At Userlike, Celery is the backbone of our application, orchestrating over a 100 million tasks per month. In this talk, I’ll share real-world insights into scaling Celery, optimizing performance, avoiding common pitfalls, handling failures, and building a resilient architecture.</p>
|
|
| /talks/8PFFPS/ |
<h3 class="talk-title">
Demystifying Design Patterns: A Practical Guide for Developers
</h3>
<h4>
Tanu
</h4>
<div class="track">Programming & Software Engineering</div>
<p class="talk-abstract">Do you ever worry about your code becoming spaghetti-like and difficult to maintain?
Master the art of crafting clean, maintainable, and adaptable software by harnessing the power of design patterns. This presentation will empower you with a clear, structured understanding of these reusable solutions to address common programming challenges.
We'll delve into design patterns’ key categories: Behavioral, Structural, and Creational, as well as explore their functionality and how they can be applied in your daily development workflow. For each category, we'll also explore a practical design pattern in detail and showcase real-world applications of these patterns, along with small-scale code examples that illustrate their practical implementation.
You'll gain valuable insight into how these patterns can translate into real-world development scenarios, such as facilitating communication between objects (Behavioral), separating interfaces from implementation for flexibility (Structural), and enabling dynamic algorithm selection at runtime (Creational).</p>
|
|
| /talks/CZXBEP/ |
<h3 class="talk-title">
Filling in the Gaps: When Terraform Falls Short, Python and Typer Step In
</h3>
<h4>
Yuliia Barabash
</h4>
<div class="track">Infrastructure - Hardware & Cloud</div>
<p class="talk-abstract">Not all resources in today’s cloud environments have native Terraform providers. That’s where Python’s Typer library can step in, offering a flexible, production-ready command-line interface (CLI) framework to help fill in the gaps. In this session, we’ll explore how to integrate Typer with Terraform to manage resources that fall outside Terraform’s direct purview. We’ll share a real-life example of how Typer was used alongside Terraform to automate and streamline the management of an otherwise unsupported API. You’ll learn how Terraform can invoke Python scripts—passing arguments and parameters to control complex operations—while still benefiting from Terraform’s declarative model and lifecycle management. We’ll also discuss best practices for defining resource lifecycles to ensure easy maintainability and consistency across deployments. By the end, participants will see how combining Terraform’s robust infrastructure-as-code approach with Python’s versatility and Typer’s user-friendly CLI can create a powerful, cohesive strategy for managing even the trickiest resources in production environments.</p>
|
|
| /talks/ABWHSD/ |
<h3 class="talk-title">
From Tensors to Clouds — A Practical Guide to Zarr V3 and Zarr-Python 3
</h3>
<h4>
Sanket Verma
</h4>
<div class="track">Data Handling & Engineering</div>
<p class="talk-abstract">A key feature of the Python data ecosystem is the reliance on simple but efficient primitives that follow well-defined interfaces to make tools work seamlessly together (Cf. http://data-apis.org/). NumPy provides an in-memory representation for tensors. Dask provides parallelisation of tensor access. Xarray provides metadata linking tensor dimensions. **Zarr** provides a missing feature, namely the scalable, persistent storage for annotated hierarchies of tensors. Defined through a community process, the Zarr specification enables the storage of large out-of-memory datasets locally and in the cloud. Implementations exist in C++, C, Java, Javascript, Julia, and Python, enabling.
This talk presents a systematic approach to understanding and implementing the newer version of [Zarr-Python](https://github.com/zarr-developers/zarr-python), i.e. Zarr-Python 3 by explaining the new API, deprecations, new storage backend, improved codec pipeline, etc.</p>
|
|
Внешние ссылки
Кол-во: 1
Ссылки на сторонние сайты
?
Исходящие внешние ссылки передают часть ссылочного веса на чужие сайты. Ссылки на авторитетные ресурсы безопасны; ссылки на мусорные сайты могут навредить репутации страницы.
Внешних ссылок на странице 1 оптимально.
Показать внешние ссылки
| Url | Анкор |
|---|---|
| pretalx.com |
Schedule
|
Конкуренты Готовность: 0%
Конкуренты в Яндексе
Кол-во: 0
Топ сайтов-конкурентов в Яндексе
?
Сайты, чаще всего появляющиеся в ТОПе Яндекса по запросам из семантического ядра этой страницы.
Мы не нашли у вас конкурентов в Яндексе. Сайт или очень молодой или плохо продвигается.
Конкурентов в ТОП-10 Яндекса не нашлось.
Конкуренты в Google
Кол-во: 0
Топ сайтов-конкурентов в Google
?
Сайты, чаще всего появляющиеся в ТОПе Google по запросам из семантического ядра этой страницы.
Конкуренты в Google тоже не найдены. Займитесь продвижением сайта!
Конкурентов в ТОП-10 Google не нашлось.
ЗоЗПП: права потребителей Готовность: 100%
Нарушения
Не выявлены
Признаков дистанционной продажи товаров (интернет-магазина) не обнаружено — требования ЗоЗПП о раскрытии информации продавца к сайту не применяются. Нарушений нет.
ФЗ-149: рекомендательные технологии Готовность: 100%
Нарушения
Не выявлены
Рекомендательные блоки («с этим покупают», «похожие товары» и т.п.) на сайте не обнаружены — требования ст. 10.7 ФЗ-149 к сайту не применяются. Нарушений нет.
ФЗ-38: реклама Готовность: 100%
Нарушения
Не выявлены
Рекламных тематик с обязательными оговорками (медицина, БАД, кредиты и займы, новостройки) на сайте не обнаружено. Нарушений нет.
ФЗ-436: защита детей Готовность: 100%
Нарушения
Не выявлены
Признаков информационной продукции (новости, видео, книги, игры, курсы) не обнаружено — обязательная возрастная маркировка по ФЗ-436 сайту не требуется. Нарушений нет.
Вердикт
Страница https://2025.pycon.de/talks/ готова к продвижению на 56%. Чтобы еще улучшить страницу и попасть на первые места поисковой выдачи необходимо:
Исправьте ошибки в мета-тегах.
Исправьте ошибки оптимизации.
Исправьте ошибки индексации.
Поделитесь с друзьями: