Анализ страницы https://2026.pycon.de/talks/
Основное Готовность: 100%
Домен
2026.pycon.de
Состояние доменного имени
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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
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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
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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
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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
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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
~0,56сек
Время отклика сервера (TTFB)
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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 0,56сек оптимальна.
Объем документа
281Кб
Размер HTML-кода страницы
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Слишком большой HTML замедляет парсинг браузером и сканирование поисковым роботом. Рекомендуется не более 200 Кб.
Объем html-документа 281Кб оптимален.
Структура html-документа корректна.
Ресурсы
Ресурсы: 5
Внешние ресурсы страницы (CSS, JS, изображения)
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Количество и тип подключённых ресурсов влияют на скорость загрузки. Большое число запросов увеличивает время рендеринга страницы.
Кол-во файлов ресурсов 5 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 | /static/js/carousel.js | |
| js | /static/js/hero-slideshow.js |
Серверные заголовки
Кол-во: 5
HTTP-заголовки ответа сервера
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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 | Mon, 24 Aug 2026 02:54:15 GMT |
| Vary | Accept-Encoding |
| ETag | "f361710f7eae60b8e303b95e50c62f40" |
| x-cache-status | MISS |
CMS
Не определена
Система управления сайтом (движок)
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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
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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
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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
PyCon DE & PyData 2026 — Europe's largest Python & AI conference. April 14-17, darmstadtium, Darmstadt. 2,000+ attendees, 100+ talks, masterclasses, sprints. We get things done.
Описание страницы в поисковой выдаче (сниппет)
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Meta Description — текст под заголовком в выдаче. Напрямую на позиции не влияет, но влияет на CTR. Оптимальная длина: 120–160 символов.
Необходимо уменьшить число символов в description (текущее значение: 185, оптимально: от 120 до 130)
Keywords
Список ключевых слов страницы (устаревший тег)
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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
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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
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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
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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: 8
Мета-теги для красивых превью в соцсетях
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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://2026.pycon.de/static/media/social_card.png |
| og:image | https://2026.pycon.de/static/media/social_card.png |
| og:description | Join PyCon DE & PyData 2026 in Darmstadt (Frankfurt), April 14-17! Germany’s largest Python, data and AI conference with talks, workshops, and the best community. Be part of the action! |
| og:url | https://2026.pycon.de/talks/ |
| og:type | article |
| og:image:width | 1200 |
| og:image:height | 630 |
Все мета-теги
Кол-во: 20
Полный список мета-тегов страницы
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Таблица всех meta-тегов, включая нестандартные. Позволяет найти опечатки, дубли и лишние теги.
Найдены мета-теги 20шт. 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://2026.pycon.de/static/media/social_card.png |
| name | description | PyCon DE & PyData 2026 — Europe's largest Python & AI conference. April 14-17, darmstadtium, Darmstadt. 2,000+ attendees, 100+ talks, masterclasses, sprints. We get things done. |
| property | og:title | Talks |
| property | image og:image:secure_url | https://2026.pycon.de/static/media/social_card.png |
| property | image og:image:secure_url | https://2026.pycon.de/static/media/social_card.png |
| property | image og:image | https://2026.pycon.de/static/media/social_card.png |
| property | image og:image | https://2026.pycon.de/static/media/social_card.png |
| property | og:description | Join PyCon DE & PyData 2026 in Darmstadt (Frankfurt), April 14-17! Germany’s largest Python, data and AI conference with talks, workshops, and the best community. Be part of the action! |
| property | og:url | https://2026.pycon.de/talks/ |
| property | og:type | article |
| property | “article:publisher“ | https://2026.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
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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
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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 145 достаточно.
Среднее число слов в абзаце 116 достаточно.
Кол-во знаков контента 139865 на странице оптимально.
Кол-во слов 20398 на странице оптимально.
Заголовки
Ошибок нет
Иерархия заголовков H1–H6
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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> 145.
Тошнота
16,97
Насколько одно слово доминирует в тексте
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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
228,40%
Насколько текст перенасыщен ключевыми словами
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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
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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 | 288 | 1,41% |
| domain | 150 | 0,74% |
| expertise | 147 | 0,72% |
| intermediate | 140 | 0,69% |
| models | 67 | 0,33% |
| novice | 66 | 0,32% |
| systems | 64 | 0,31% |
| learning | 61 | 0,30% |
| engineering | 56 | 0,27% |
| practical | 52 | 0,25% |
| building | 45 | 0,22% |
| language | 43 | 0,21% |
| software | 40 | 0,20% |
| programming | 39 | 0,19% |
| machine | 37 | 0,18% |
| testing | 33 | 0,16% |
| agents | 33 | 0,16% |
| without | 32 | 0,16% |
| pipelines | 32 | 0,16% |
| explore | 30 | 0,15% |
Индексация Готовность: 0%
Индексирование
Есть ошибки
Разрешено ли индексирование страницы
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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 212 слишком много. Проведите ревизию и оптимизацию ссылок сайта.
Robots.txt
Найден корректный robots.txt
Файл управления сканированием сайта роботами
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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: 385 байт. Загружен за: 0сек.
Проверяемая страница не запрещена в robots.txt.
Robots.txt доступен по постоянному адресу
Показать содержимое robots.txt
User-agent: LinkedInBot
Allow: /certificate-share/
User-agent: Twitterbot
Allow: /certificate-share/
User-agent: facebookexternalhit
Allow: /certificate-share/
User-agent: WhatsApp
Allow: /certificate-share/
User-agent: TelegramBot
Allow: /certificate-share/
User-agent: *
Disallow: /certificate-share/
Disallow: /certificate-validate/
Sitemap: https://2026.pycon.de/sitemap.xml
Sitemap
Кол-во: 1
XML-карта сайта для поисковиков
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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 содержит карту сайта. Это прекрасно!
Robots.txt не содержит ошибок в карте сайта.
Показать карту сайта
| Url | Статус |
|---|---|
| https://2026.pycon.de/sitemap.xml |
|
Внутренние ссылки
Кол-во: 199
Ссылки на другие страницы своего сайта
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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 199 слишком много. Проведите оптимизацию сайта!
Внутренние ссылки не запрещены к индексации в robots.txt.
Показать первые 100 внутренних ссылок
| Url | Анкор | Состояние |
|---|---|---|
| / |
PyCon DE & PyData 2026
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| /talks/ |
Talks
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| /masterclasses/ |
Masterclasses
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| /blog/ |
Blog
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| /faqs/ |
FAQs
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| /blog/pyladies/ |
PyLadies
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| /sprints/ |
Sprints
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| /sponsors/ |
Sponsors & Partners
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| /venue/ |
Venue
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| /team/ |
Team
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| /buy-ticket/ |
Get Tickets
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| / |
Home
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| /buy-ticket/ |
Tickets
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| /talks/ |
Talks
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| /masterclasses/ |
Masterclasses
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| /blog/ |
Blog
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| /faqs/ |
FAQs
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| /blog/pyladies/ |
PyLadies
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| /blog/lightning-talks/ |
Lightning Talks
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| /sprints/ |
Sprints
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Sponsors & Partners
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| /sponsoring/ |
Sponsoring
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| /speaker-briefing/ |
Speaker Briefing
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| /financial-aid/ |
Financial Aid
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| /media-kit/ |
Media Kit
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Venue
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Team
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Code of Conduct
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| /imprint/ |
Imprint
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| /newsletter/ |
Newsletter
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| /contact/ |
Contact
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| /privacy-policy/ |
Privacy Policy
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| /talks/3JLSEF/ |
<h3 class="talk-title">
Catch the LLM if you Can: Watermarking LLMs
</h3>
<h4>
Subhosri Basu
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Ethics & Privacy</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Intermediate</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Intermediate</span>
</div>
</div>
<p class="talk-abstract">With Large Language Models (LLMs), generating high-quality text and images is easy and so is
misusing it. As AI-generated content becomes harder to distinguish from human generated content,
developers are increasingly asking: How can we verify whether a piece of text comes from an LLM?
We’ll explore Python’s simplicity and rich ecosystem of libraries to solve this problem.
This talk introduces the foundations of LLM watermarking and shows how developers can implement
these techniques entirely in Python. We’ll discuss two core approaches, EXP sampling method and
KGW method. We will go through the implementation of the KGW method using simple,
transparent code, and compare it with the EXP approach. There's no need for a large model or a GPU
cluster to understand how these systems work and the core ideas can be implemented in pure
Python using simple code. The code repositories, which includes both methods will be provided so
that the attendees can follow along.
Along the way, we’ll discuss the trade-offs and the limitations of current research. And for those
wondering, “Do I have to implement all this myself?”, the talk concludes with a quick overview of MarkLLM, an existing open-source toolkit that provides a unified Python interface for experimenting with watermarking algorithms.
Attendees will leave with a clear understanding of how watermarking works, when it’s useful, and
how to integrate these techniques into real-world Python projects.</p>
|
|
| /talks/7JXYKH/ |
<h3 class="talk-title">
Offline Fallback for a Mobile LoRaWAN Gateway
</h3>
<h4>
Jannis Lübbe
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Infrastructure - Hardware & Cloud</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Intermediate</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Novice</span>
</div>
</div>
<p class="talk-abstract">LoRaWAN gateways typically depend on cloud-based network servers, creating a vulnerability during internet outages. This talk presents a hybrid solution: a Raspberry Pi-based mobile gateway that operates on The Things Stack Sandbox while simultaneously decoding all device messages locally.
The system leverages existing network infrastructure for broad coverage during normal operation, while maintaining full local data access when connectivity fails. This is particularly valuable for emergency response scenarios and remote monitoring where sensor data must remain available regardless of network conditions.
The implementation uses Python for gateway orchestration and API integration, while incorporating existing JavaScript libraries (`lora-packet` and device decoders) for LoRaWAN decryption and payload decoding. Data is stored locally in SQLite for reliability and easy access.</p>
|
|
| /talks/GCGLPN/ |
<h3 class="talk-title">
pytest tips and tricks for a better testsuite
</h3>
<h4>
Freya Bruhin
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Programming & Software Engineering & Testing</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Intermediate</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Intermediate</span>
</div>
</div>
<p class="talk-abstract">pytest lets you write simple tests fast - but also scales to very complex scenarios: Beyond the basics of no-boilerplate test functions, this training will show various intermediate/advanced features, as well as gems and tricks.
To attend this training, you should already be familiar with the pytest basics (e.g. writing test functions, parametrize, or what a fixture is) and want to learn how to take the next step to improve your test suites.
If you're already familiar with things like fixture caching scopes, autouse, or using the built-in `tmp_path`/`monkeypatch`/... fixtures: There will probably be some slides about concepts you already know, but there are also various little hidden tricks and gems I'll be showing.</p>
|
|
| /talks/37AESH/ |
<h3 class="talk-title">
In Praise of Documentation: Tools, Tips & Techniques for Literate Programming in the AI Age
</h3>
<h4>
Stephen
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Education, Career & Life</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Novice</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">None</span>
</div>
</div>
<p class="talk-abstract">This talk has one simple message: *please document your code*. If you attend my talk, you'll hear me explain why I praise documentation, and why you should too.
While writing documentation is generally acknowledged to be a "good thing", most engineers do not document their work. I'll offer my optionated lament on the life and death of literate programming. A lament is a poetic discourse, expressing sadness, or feeling sorry about something. I'll give some examples of the *bad things* that can happen when people don't write documentation.
Then, after making you feel bad, I'll give examples of how you can *feel good*. I'll explain why writing documentation is a "good" edifying activity, which helps you to be a better person, and make a better world.
I'll review types of open source documentation (Python and Unix), documentation frameworks (Diátaxis), and Python tools (Sphinx, Jupyter, Quarto) you can try out as soon as my talk is finished.
Then, I'll get "cool n' futuristic" by talking about AI. I'll emphasise the importance of text to AI-assisted coding and agentic workflows for "spec-driven development" (e.g. Agent-OS with Claude Code), before tempering your excitement by giving you some old-fashioned advice on "good" writing style by George Orwell.
In summary, if you come to my talk, you might experience an unusual mixture of sadness combined with hope. To conclude, I'll tell you to "please document your code". You'll laugh, go to the next talk, and forget my advice.</p>
|
|
| /talks/EL7X8C/ |
<h3 class="talk-title">
Hierarchical Models in MMM: Can Structure beat data size?
</h3>
<h4>
Mohamed Amine Jebari
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Machine Learning & Deep Learning & Statistics</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Intermediate</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Intermediate</span>
</div>
</div>
<p class="talk-abstract">In every marketing project, teams strive to find more data, a longer timeframe, and more detailed splits, just to fix noisy channel attribution.
But what if structure played a bigger role than size and volume?
In this talk, we try to prove this. Using a simple toolkit like Arviz and PyMC, we show you a simple hierarchical mix model, and how, by applying partial pooling, we can stabilize important KPIS like ROAS estimates across sparse channels- without the need for more data.
We will go through the code, transformation, and the real-life practices that allow us to get as close to the truth, to be able to have a meaningful impact in the marketing world.
The approach will be centered around marketing mix models, different transformations, and how useful it will be for the business.</p>
|
|
| /talks/JBFGCA/ |
<h3 class="talk-title">
Building Agentic Systems with Python, LangGraph, MCP, and A2A
</h3>
<h4>
Holger Nösekabel
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Autonomous Systems & AI Agents</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Intermediate</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Intermediate</span>
</div>
</div>
<p class="talk-abstract">Building an agentic system that collects and evaluates company information in real time—without curated datasets—requires solving difficult challenges in data acquisition, quality control, and agent orchestration.
This talk outlines the solution design for such a system, implemented with Python-based tooling including LangGraph, and emerging protocols such as A2A and MCP, within a multi-agent workflow. Because MCP and A2A are still new and lightly documented, we will share implementation lessons and a practical example of a hub-and-spoke architecture based on a recent real-world system.
Attendees will learn architectural patterns for multi-agent systems, common pitfalls of using MCP/A2A in real-world scenarios, and strategies for maintaining data quality in agent-based workflows.</p>
|
|
| /talks/BHJERV/ |
<h3 class="talk-title">
Django-Q2: Async Tasks Made Simple
</h3>
<h4>
Moin Uddin
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Django & Web</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Novice</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Novice</span>
</div>
</div>
<p class="talk-abstract">Managing asynchronous task queues in Django with tools like Celery can be overkill for many projects. Django-Q2 is a lightweight alternative that integrates natively with the Django admin. In this talk, you will learn how to streamline your background tasks and cron jobs, featuring a practical demo to get you started immediately.</p>
|
|
| /talks/FJQXEQ/ |
<h3 class="talk-title">
Increase productivity of CNC-machining of aerospace engine parts with Python
</h3>
<h4>
Nico Buhl
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Programming & Software Engineering & Testing</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">None</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">None</span>
</div>
</div>
<p class="talk-abstract">Increasing unit labour costs and the imperative need to reduce energy consumption raises the necessity to enhance productivity in industrial production. Python is an excellent tool for GKN Aerospace, as the world’s leading tier one aerospace supplier, to address the needs for higher utilization and unmanned operation on the shopfloor on its site in Kongsberg, Norway.
As an example, the presentation shares insight into the in-house developed “Production Execution System”, consisting of a Python backend and a REACT frontend. The application orchestrates all necessary data on cell-level, like NC-programs and additional digital services of the company’s IT environment during unmanned production. Furthermore, it supports the operator with necessary information to ensure highest quality of engine parts in a work environment of increasing digitalization and workload.</p>
|
|
| /talks/Q9DU8N/ |
<h3 class="talk-title">
Causal Inference through the lens of probabilistic programming
</h3>
<h4>
Dr. Juan Orduz
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Machine Learning & Deep Learning & Statistics</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Intermediate</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Intermediate</span>
</div>
</div>
<p class="talk-abstract">Causal inference asks the hardest question in data science: "What would have happened if things were different?" While traditional methods often rely on rigid rules, statistical tests or "black box" adjustments, Probabilistic Programming Languages (PPLs) like PyMC and NumPyro offer a transparent, flexible, and powerful lens to view these problems.
In this talk, we move beyond the standard "correlation is not causation" disclaimer. We will build a unified workflow that starts with robust A/B testing, moves to bias adjustment in observational data using multilevel models, and culminates with advanced Deep Causal Latent Variable Models (CEVAE).</p>
|
|
| /talks/PARU7X/ |
<h3 class="talk-title">
From Struggling to Mastery: A Practical Guide to Data Pipeline Operations
</h3>
<h4>
Akif Cakir
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Data Handling & Data Engineering</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">None</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Intermediate</span>
</div>
</div>
<p class="talk-abstract">How mature are your data pipeline operations? A Roadmap to Operational Excellence.
Data teams often struggle to scale their pipeline operations, trapped in a cycle of manual fixes and reactive fire-fighting. But what does "good" actually look like? In this talk, we introduce a standardized 5-level maturity model for Data Operations, focusing on three critical pillars: Orchestration, Data Quality, and Data SLOs.
We will deconstruct the journey from "Struggling" (manual scripts, no guarantees) to "Mastery" (automated, resilient, and measured). Attendees will leave with a concrete framework to assess their team’s current standing and a clear, step-by-step roadmap to raise the bar toward operational excellence.</p>
|
|
| /talks/HQBC7R/ |
<h3 class="talk-title">
Rediscovering single-node processing: When does it make sense to move from Spark to Polars?
</h3>
<h4>
Jonas Böer
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Data Handling & Data Engineering</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Novice</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Intermediate</span>
</div>
</div>
<p class="talk-abstract">As data engineers, we are used to spinning up a Spark Cluster every time we want to do data processing and handle the overhead that comes with using such a mighty framework. But is this really necessary? In this talk I will argue that single-node processing with Polars is in many cases easier and cheaper. I will compare a typical ETL & Feature Engineering task in Spark and in Polars and offer a pragmatic opinion on when to use one or the other.</p>
|
|
| /talks/MQJVFU/ |
<h3 class="talk-title">
AI Evals Done Right: From Vibes to Confident Decisions
</h3>
<h4>
Martin Seeler
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Generative AI & Synthetic Data</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">None</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Intermediate</span>
</div>
</div>
<p class="talk-abstract">Testing traditional software is "simple"... same input, same output. LLMs? Not so much. Same prompt, different result every time. So how do you actually know if your AI product is good?
Most teams struggle with this. Generic metrics like "Helpfulness: 4.2" sound scientific but don't drive real decisions. And when a new model releases, it's weeks of debates instead of data.
This talk introduces Error Analysis: a methodology to discover the concrete failure modes of your AI product and turn them into measurable evals. You'll learn how to build a failure taxonomy that enables real prioritization. Which issues are critical? Which are frequent? What should developers fix next, and how do you measure success?
The payoff: A real quality number for stakeholders. Concrete improvement tasks for developers. And when a new model drops, a ship-or-skip decision within 24 hours based on actual data.
Expect a meme-powered walkthrough, real-world examples from production, and a clear path to implement this yourself starting with just 20 traces.</p>
|
|
| /talks/LVJXK3/ |
<h3 class="talk-title">
Using Sensor Fusion and ML to Navigate Underground When GPS Fails
</h3>
<h4>
Étienne Tremblay
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Machine Learning & Deep Learning & Statistics</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Intermediate</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Intermediate</span>
</div>
</div>
<p class="talk-abstract">In the twisting vaults of a subway, metro, or U-Bahn, there’s often no reliable cell service, wifi, or GPS. Which means riders had no good way of keeping track of their stops or ETA when underground.
After collecting extensive ground truth data, we trained a motion classifier using the phone's accelerometer to identify a moving train. This prediction is fed into a location model that combines it with the train schedule to estimate a location, even when GPS fails. We cover our unique data pipeline, feature engineering, and the optimization for high-scale, offline edge deployment to millions of users.</p>
|
|
| /talks/FP7YN7/ |
<h3 class="talk-title">
Are we free-threaded ready? Looking at where free-threaded Python fails
</h3>
<h4>
Cheuk Ting Ho
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Python Language & Ecosystem</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Advanced</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Advanced</span>
</div>
</div>
<p class="talk-abstract">Free-threaded Python aims to significantly improve performance, allowing multiple native threads to execute Python bytecode concurrently. In this talk, we will explore the current state of Python's free-threading initiative and assess its practical readiness for widespread adoption.</p>
|
|
| /talks/QX8DDJ/ |
<h3 class="talk-title">
Do you know how well your model is doing? Evaluate your LLMs
</h3>
<h4>
Cheuk Ting Ho
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Natural Language Processing & Audio (incl. Generative AI NLP)</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Advanced</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Advanced</span>
</div>
</div>
<p class="talk-abstract">Large Language Models (LLMs) are becoming central to modern applications, yet effectively evaluating their performance remains a significant challenge. How do you objectively compare different models, benchmark the impact of fine-tuning, or ensure your LLM responses adhere to safety guidelines (guard-railing)? This hands-on workshop addresses these critical questions.</p>
|
|
| /talks/BQYTVM/ |
<h3 class="talk-title">
Beyond Stateless: Why Your Web Service Architecture is Fighting Against Performance
</h3>
<h4>
Heiner Wolf
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Programming & Software Engineering & Testing</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Intermediate</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Intermediate</span>
</div>
</div>
<p class="talk-abstract">We've been told for years that stateless services are the holy grail of
scalable web architectures. But what if this foundational principle is
actually hurting development speed and runtime performance?
Coding agents follow our example. They do what we would have done, only
10 times more. They also apply the "stateless is good" myth.
This talk challenges the dominant paradigm by demonstrating how
stateful, object-oriented programming can automatically scale to
millions of users without the typical infrastructure complexity.
I'll show how keeping objects with their state in distributed memory
eliminates the need for explicit caching strategies, reduces database
bottlenecks, and dramatically simplifies your code base. You'll see how
a simple Python class can transparently scale across multiple servers,
handling millions of concurrent users without implementing REST
endpoints, message queues, or cache invalidation logic.
So you can guide your agent to do scalability the right way.</p>
|
|
| /talks/GVHZW9/ |
<h3 class="talk-title">
Solving Marketplace Cold Start at Scale with Ranking
</h3>
<h4>
Theodore Meynard
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Machine Learning & Deep Learning & Statistics</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">None</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Advanced</span>
</div>
</div>
<p class="talk-abstract">Cold start is a critical bottleneck for marketplaces: new items lack behavioral signals and reviews, so ranking models under-expose them, delaying the very signals needed to rank them well. This talk shares practical solutions developed at scale for a travel marketplace, including guaranteed exposure at key positions, efficient real-time re-ranking, and targeted boosting for unactivated items. Attendees will learn how experiment-driven iteration shaped a robust system that accelerates early traction for new items without sacrificing overall marketplace health.</p>
|
|
| /talks/WQGXJ3/ |
<h3 class="talk-title">
Exploring Germany's Urban Geography with Census and OpenStreetMap Data
</h3>
<h4>
Travis Hathaway
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Data Handling & Data Engineering</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Novice</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Novice</span>
</div>
</div>
<p class="talk-abstract">When conducting studies of the urban form, an important resource many researchers turn to is the massive OpenStreetMap dataset. But, as extensive as this dataset is, it lacks one very important aspect about the cities it covers: the people who live there. In this talk, I show you how to add this missing element to your research by bringing in German Census data to create rich analysis capable of answering some of the most pressing issues facing our cities today. I exemplify this by walking you through my own research in urban geography and sustainability with a study of how equitably distributed emergency care hospitals are in cities across Germany. Throughout, we look at how Python and PostgreSQL can be used as effective tools to enable this research and keep it organized.</p>
|
|
| /talks/93SXWY/ |
<h3 class="talk-title">
Ship Data with Confidence: Declarative Validation for PySpark & Pandas
</h3>
<h4>
Ryan Sequeira
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Data Handling & Data Engineering</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Intermediate</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Intermediate</span>
</div>
</div>
<p class="talk-abstract">Tired of data quality issues crashing your PySpark and Pandas pipelines? This talk introduces [dataframe-expectations](https://github.com/getyourguide/dataframe-expectations), a lightweight, open-source library for declarative data validation. We will dive into the library's design and demonstrate how to easily define and apply data quality expectations to catch errors early, reduce debugging time, and ship more reliable data products, faster. Learn to build more robust data pipelines and move from reactive problem-solving to proactive data validation.</p>
|
|
| /talks/NF7MKB/ |
<h3 class="talk-title">
Programming Quantum Networks in Python
</h3>
<h4>
Samuel Oslovich
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Programming & Software Engineering & Testing</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Novice</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">None</span>
</div>
</div>
<p class="talk-abstract">Quantum networks connect quantum devices including quantum computers, enabling applications not realizable in classical networks, such as secure quantum computing in the cloud and quantum key distribution. These networks are now moving from theory to reality, and as part of the Quantum Internet Alliance, we are actively building a prototype quantum network in Europe, driven by applications developed in Python.
In this talk, we will introduce quantum networking and demonstrate how to program quantum network applications in Python by walking through the quantum teleportation protocol. We'll conclude by sharing resources so that you can begin experimenting with quantum network programming yourself. No prior quantum experience required.</p>
|
|
| /talks/TZYGTL/ |
<h3 class="talk-title">
5 Years of NiceGUI: What We Learned About Designing Pythonic UIs
</h3>
<h4>
Falko Schindler
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Programming & Software Engineering & Testing</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Intermediate</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Intermediate</span>
</div>
</div>
<p class="talk-abstract">NiceGUI has grown from a small experiment into a widely used framework for building modern web-based user interfaces entirely in Python. After five years of development, thousands of users, and countless design iterations, we have gathered a rich set of insights into what makes a UI framework feel truly “Pythonic” while still leveraging the power of the web platform.
This talk presents the key lessons learned while evolving NiceGUI, with a focus on how Python’s own language features can meaningfully improve the developer experience. We explore how context managers, method chaining, decorators, async/await, type hints, dataclasses, and even well-chosen default arguments contribute to a clean, expressive, and maintainable UI API. Attendees will walk away with a deeper understanding of how to design Python-first interfaces—whether for web apps, dashboards, or internal tools—without needing to write JavaScript, CSS, or frontend boilerplate.</p>
|
|
| /talks/9ZKYRD/ |
<h3 class="talk-title">
A minimalist introduction to Ansible
</h3>
<h4>
Raniere Silva
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">MLOps & DevOps</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Novice</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Novice</span>
</div>
</div>
<p class="talk-abstract">[Ansible](https://docs.ansible.com/) is a popular [infrastructure as code](https://en.wikipedia.org/wiki/Infrastructure_as_code) tool for server configuration and software deployment. This tutorial will cover things that I wish the first day that I started using Ansible to manage the projects at my work.</p>
|
|
| /talks/APWGQB/ |
<h3 class="talk-title">
From Pixel to Payouts: A Multi-Agent System for Real-Time Insurance Claims Processing
</h3>
<h4>
Claudio Giorgio Giancaterino
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Autonomous Systems & AI Agents</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Intermediate</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Intermediate</span>
</div>
</div>
<p class="talk-abstract">The traditional process for auto damage evaluation is relatively slow, subjective, and prone to fraud. With this presentation, the goal is to show a Multi-Agent System designed for the automation and standardization in real-time of the car damage evaluation, disrupting the initial claims workflow. The system is built around an Orchestrator Agent with the role to coordinate specialized AI agents: a Vision Agent (powered by OpenAI GPT-5.2) for damage analysis and severity classification, two Cost Estimation Agents (powered by Perplexity's sonar-pro) to provide comparative quotes (OEM vs. Aftermarket), and a Shop Finder Agent for local repair options. The system produces a report that includes a description of the damage, severity, comparative repair costs in local currency, and recommended repair shops, all embedded into a Gradio/Streamlit interface. The task of this approach is to reduce the processing time, improve transparency for customers, and provide insurers with objective data to enable faster claims resolution.</p>
|
|
| /talks/GATMPP/ |
<h3 class="talk-title">
Surviving AI Fatigue: Staying Sane and Relevant in a Fast Moving Field
</h3>
<h4>
Ajay, Jeyashree Krishnan
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Education, Career & Life</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Novice</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Novice</span>
</div>
</div>
<p class="talk-abstract">In an era where new AI models, benchmarks, and frameworks emerge daily, many of us feel caught in a relentless cycle of catching up, what is called "AI fatigue". This talk dives into the causes and consequences of that fatigue, from information overload and social media hype to the constant pressure to stay relevant. Drawing on personal experience and community insights, we explore why chasing every new paper or trend often leads to burnout rather than mastery.
More importantly, we share practical, evidence-backed strategies to stay informed without losing balance: curating a focused “information diet,” setting clear boundaries, using summarization tools intelligently, maintaining a personal knowledge base, and embracing “JOMO”—the joy of missing out. We also discuss how organizations can combat fatigue structurally by promoting focus, curiosity, and psychological safety.
This session is for anyone, from beginners to seasoned professionals, seeking to rediscover genuine curiosity in AI while preserving mental well-being. Attendees will leave with concrete tools, actionable habits, and a renewed sense that it is not only acceptable but healthy to not know everything.</p>
|
|
| /talks/GBKUNF/ |
<h3 class="talk-title">
How to create effective data visualizations
</h3>
<h4>
Dominik Haitz
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Visualisation & Notebooks</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Novice</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Novice</span>
</div>
</div>
<p class="talk-abstract">What distinguishes a lousy plot from a beautiful chart that communicates insights effectively? This talk will show you the underlying principles of good data visualization, offer lots of practical tips and tricks and give an overview of the data visualization landscape in Python.
After the talk, you will be able to create better charts, whether for exploring your own data or for communicating results to others.</p>
|
|
| /talks/TRGQTL/ |
<h3 class="talk-title">
Open Table Formats in the Wild™ - Reloaded: Vortexing Ducks over Floating Icebergs
</h3>
<h4>
Franz Wöllert
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Data Handling & Data Engineering</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Novice</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Intermediate</span>
</div>
</div>
<p class="talk-abstract">Open table formats have *almost* freed us from vendor lock-in. They form a critical building block of the modern, composable data stack. The most prominent open table format is Apache Iceberg - not only because of its storage layout, but also due to its REST catalog specification. Iceberg has gained significant traction through a recent stream of feature announcements from the community itself, major cloud providers like AWS, and data platform leaders such as Snowflake and Databricks.
But cutting through the hype: how does Iceberg actually perform in the real world if you are *not* Netflix or Apple which are capable of *Building Your Own Snowflake* (BYOS)? Can you realistically migrate from legacy solutions to Iceberg and enjoy all its promises without tradeoffs?
That, of course, is a rhetorical question. Some even argue that Iceberg got parts of the specification fundamentally wrong!?!
Curious? Join me for another episode of Open Table Formats in the Wild™. Expect a practical look at the current state of Apache Iceberg and Apache Parquet, alongside a gentle introduction to DuckLake and Vortex as promising contenders for table and file formats, respectively.</p>
|
|
| /talks/BRCNB7/ |
<h3 class="talk-title">
(Autism and) The Predictive Brain Theory (in Tech)
</h3>
<h4>
Dennie Declercq
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Education, Career & Life</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">None</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">None</span>
</div>
</div>
<p class="talk-abstract">New studies showed how the brain is not a passive receiver of stimuli but an active predictor of stimuli. People with autism have more difficulties when the predicted and received stimuli doe not match. How do we create a tech workforce where autistic individuals can work more comfortable due to predictability?</p>
|
|
| /talks/EXXWMV/ |
<h3 class="talk-title">
Black Hole Stars: An Astronomical Mystery (Mostly) Solved with NumPyro and JAX
</h3>
<h4>
Raphael Hviding
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">PyData & Scientific Libraries Stack</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Novice</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">None</span>
</div>
</div>
<p class="talk-abstract">The James Webb Space Telescope has revealed a mysterious population of "Little Red Dots": extremely distant objects that have upended our understanding of the early Universe. However, revealing the true nature of these marvels requires computationally-intensive statistical modeling of complex astronomical data. In this talk, we explore how we used JAX and NumPyro to help solve this puzzle. We will introduce these powerful Python tools, demonstrate how they accelerate complex statistical data analysis, and show how they provided evidence that Little Red Dots may in fact be "Black Hole Stars."</p>
|
|
| /talks/CVPVPK/ |
<h3 class="talk-title">
Beyond Vibe-Coding: A Practitioner's Guide to Spec-Driven Development in AI Engineering
</h3>
<h4>
Alina Dallmann
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Programming & Software Engineering & Testing</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Intermediate</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Novice</span>
</div>
</div>
<p class="talk-abstract">AI-assisted coding became the default. Tools like GitHub Copilot, Cursor, and Claude can generate hundreds of lines of Python in seconds. However, the real challenge isn't how fast we generate code — it's how we ensure that generated code actually represents our intent, follows best practices, and integrates cleanly into existing systems.
In this talk, I present Spec-Driven Development (SDD), a way to engineer the context in which AI writes code. Using a realistic example from my work building production-grade retrieval-augmented generation systems, I show how specifications can become a practical way to interact with AI coding tools — grounded in a concrete use case, from spec to implementation.</p>
|
|
| /talks/TST9LF/ |
<h3 class="talk-title">
Dynamic Knowledge Graphs
</h3>
<h4>
Jakob Leander Müller
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Data Handling & Data Engineering</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Novice</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Intermediate</span>
</div>
</div>
<p class="talk-abstract">Traditional RAG systems struggle to understand holistic connections in distributed, constantly changing knowledge sources that characterize real-world organizations. While document-based approaches using vector embeddings provide basic retrieval, they fail to capture relationships and answer complex questions about interconnected information. Graph-based RAG offers a solution, but existing implementations like Microsoft's GraphRAG explicitly avoid dynamic operations due to complexity, requiring costly rebuilds when knowledge changes.
This talk introduces a production-ready dynamic knowledge graph system that supports real-time insertion, querying, and deletion of information. Through practical implementation details you will learn to build maintainable knowledge graphs that evolve with data, handle ambiguous entities and preserve information lineage.</p>
|
|
| /talks/3XDMXS/ |
<h3 class="talk-title">
From Research Models to SLAs: Operationalizing TSFMs with Python
</h3>
<h4>
Jeyashree Krishnan, Catarina Filipe
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">MLOps & DevOps</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Intermediate</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Advanced</span>
</div>
</div>
<p class="talk-abstract">Time series foundation models (TSFMs) such as Chronos, Lag-Llama, TimesFM, and Siemens’ own GTT have shown strong generalization capabilities across diverse forecasting tasks. However, integrating these models into a large organization is primarily a software engineering and MLOps challenge rather than a modeling one.
In this talk, we present a real-world case study based on Siemens KPI Forecast, a Python-based forecasting platform that operationalizes multiple TSFMs as reusable, production-grade services. The platform integrates both open research models and Siemens-developed models behind a unified API, supporting zero-shot inference, fine-tuning jobs, and fine-tuned inference depending on user needs and operational constraints.
We focus on how Python is used to compose heterogeneous components including open and closed-source models, internal data products, APIs, and orchestration layers into a consistent time series specialist user experience. The session also covers challenges operating such services within a B2B environment, including issues related to monitoring, versioning, and governance.
Attendees will gain practical insights into turning TSFMs into reliable Python services that scale across teams and use cases.</p>
|
|
| /talks/QMBEZX/ |
<h3 class="talk-title">
Is digital sovereignty a new buzzword in AI development?
</h3>
<h4>
Dr. Maria Börner
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Ethics & Privacy</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">None</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">None</span>
</div>
</div>
<p class="talk-abstract">AI development usually focuses on feasibility and implementation, but a new buzzword is now being used: 'sovereignty'. While customers are excited about it, what does it mean for them and for AI developers? In this presentation, we analyse different aspects of sovereignty and explore how it can be used to build trustworthy AI solutions.
We will also discuss current examples from politics and development to identify the best practices for secure data processing.</p>
|
|
| /talks/NAHX3L/ |
<h3 class="talk-title">
Restaurants around train stations are bad and I can prove it
</h3>
<h4>
Dennis Schulz
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Machine Learning & Deep Learning & Statistics</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">None</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Novice</span>
</div>
</div>
<p class="talk-abstract">Have you ever asked yourself: Why is there no good food option close to this main station? This talk tries to find out if this is a systematic problem - using publicly available data and Google APIs.
After this talk, you will know about the best- and worst-rated restaurants close to main stations in Germany, if kebabs or pizza places are systematically a better choice, and which station is the worst to eat in all of Germany.</p>
|
|
| /talks/MLUK9M/ |
<h3 class="talk-title">
Why Did The Model Do That? Debugging the Ghost in the Machine
</h3>
<h4>
Cosima Meyer
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Ethics & Privacy</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Intermediate</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Intermediate</span>
</div>
</div>
<p class="talk-abstract">Why did the model say "No"? In an era where machine learning models increasingly influence high-stake decisions, "trust me" isn't a sufficient explanation. Yet, the logic behind many model decisions remains a black box, often hiding bias and making it difficult to establish trust.
In this talk, we move beyond the mystery of the "ghost in the machine" and into practical debugging using a structured *XAI Decision Tree*. Instead of guessing which method to use, we will walk through a logical framework that narrows down the field based on a few critical questions: the type of data you have, the level of model access available, and whether you need to explain a single prediction or the entire system.
The audience will leave with a clear path to choosing the right explainable AI (XAI) method - such as SHAP, LIME, or Integrated Gradients - and the corresponding Python framework for their specific use case.
This session will cover:
- Importance of XAI: Understanding why XAI is crucial using a real-world example
- XAI Landscape: An overview of existing XAI methods and how they are related
- XAI Decision Tree: How to use the structured XAI decision tree to choose the right explanation method for your use case
- Local vs global: A common understanding of local vs global explainability
- XAI in Practice: XAI in practice as well as corresponding Python frameworks to use</p>
|
|
| /talks/GPJGH3/ |
<h3 class="talk-title">
Building reliable data pipelines with polars and dataframely
</h3>
<h4>
Oliver Borchert, Andreas Albert
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Data Handling & Data Engineering</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Intermediate</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Novice</span>
</div>
</div>
<p class="talk-abstract">If you have worked with real-world data before, you know that processing it can be challenging. Data often comes scattered across tables, in inconsistent encodings, with duplicated rows and is generally dirty. In this tutorial, you will learn how to process large amounts of data reliably and quickly using `polars` and `dataframely`.
What we love about `polars` is that it's easy to use, fast and elegant — it allows us to build and compose complex transformations with ease. On this basis, we built `dataframely`: a library for defining and validating contents of polars data frames. With `dataframely`, we can build pipelines without ever getting confused about what's in our data frames. We document and validate our expectations and assumptions clearly, which makes our pipeline code simpler and easier to understand. "Is this join correct?", and "where did this column come from?" are questions you will not have to worry about anymore.
In this tutorial, you will become familiar with `polars` basics by writing a simple pipeline: you will read data, transform it to make it ready for use, and you will learn how to do that fast. With `dataframely` schemas, you will upgrade your code from "it works" to "it's beautiful!", and along the way, `dataframely` will help you eliminate entire classes of bugs you will never have to think about again. After the tutorial, you will be all set to use these tools in your own work.</p>
|
|
| /talks/V7LQGR/ |
<h3 class="talk-title">
Don’t Let Imposter Syndrome Win: U Can Do Big Things from a Small Place, A 7-Year African AI Journey
</h3>
<h4>
Gift Ojeabulu
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Community & Diversity</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">None</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Novice</span>
</div>
</div>
<p class="talk-abstract">Imposter syndrome affects engineers everywhere, but underrepresented professionals often face amplified self-doubt due to geography, limited access, and systemic biases. In this talk, I share my 7-year journey as an African AI engineer building global impact from outside major tech hubs. From founding DataFestAfrica and leading remote AI opportunities to getting the attention of organizations like Huawei, MongoDB, McKinsey, and AnyScale, I’ll show how community, mentorship, open source, media presence, and strategic partnerships can create opportunities and influence. Attendees will gain practical strategies to overcome self-doubt, expand their reach, and make a meaningful difference in tech, no matter where they are.</p>
|
|
| /talks/ZYUJH3/ |
<h3 class="talk-title">
How to Search Through 800 Billion Records in Real Time
</h3>
<h4>
Mirano Tuk, Filip Bacic
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Data Handling & Data Engineering</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Intermediate</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Intermediate</span>
</div>
</div>
<p class="talk-abstract">Large-scale distributed systems rarely produce clean data streams. In practice, hundreds of services continuously emit overlapping updates, retries, corrections, and partial state. Turning that constant stream of noisy events into a reliable, searchable dataset in real time, while processing hundreds of billions of records per day, requires careful architectural choices.
This talk shares practical lessons from building a Kafka-based ETL pipeline that transforms massive volumes of events into a coherent dataset suitable for real-time search. After a brief overview of the system architecture, we focus on several key techniques: reducing redundant processing through key deduplication and short-lived buffers, defining when messages can be safely acknowledged without risking data loss, and keeping long-running ETL services healthy under heavy Kafka workloads.
The session emphasizes concrete engineering trade-offs and operational realities rather than theory. Attendees will leave with practical patterns for building more reliable and efficient streaming pipelines.</p>
|
|
| /talks/GYBRVN/ |
<h3 class="talk-title">
Making Tech Tutorials Accessible: Practical Techniques for Educators
</h3>
<h4>
Tamara Badikyan
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Education, Career & Life</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">None</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Novice</span>
</div>
</div>
<p class="talk-abstract">Want to make your tech tutorials accessible but don't know where to start? This talk shares practical techniques anyone can use.
In June 2025, I started creating tutorials for deaf and hard-of-hearing learners because my partner is hard of hearing. I learned that accessible content helps everyone: international learners, people on noisy trains, junior developers and tired seniors at the end of the day.
In this talk, I will share practical techniques for creating accessible tech tutorials:
• Creating videos with meaningful subtitles (manual timing, simple language)
• Principles of simple language for technical content
• Structuring content so everyone can navigate it easily
I am a content creator who learned these techniques through experimentation while teaching Excel. The talk presents my actual workflow with examples from creating tutorials for deaf/hard-of-hearing learners.
Whether you're creating video tutorials, writing documentation, or teaching workshops, you'll leave with actionable steps to make your content more accessible.
Why it matters: Tech education is growing globally. Making our content accessible isn't just good ethics—it makes our teaching better for everyone.</p>
|
|
| /talks/S9VSCV/ |
<h3 class="talk-title">
Roll for Architecture: DungeonPy – A D&D Companion as Server + Thin Clients
</h3>
<h4>
Francesco Conte
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Programming & Software Engineering & Testing</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Intermediate</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Intermediate</span>
</div>
</div>
<p class="talk-abstract">### **DungeonPy** – an interactive Dungeon&Dragons app for remote campaigns
As a matter of fact, tabletop RPGs are secretly distributed systems: one canonical world state, many clients, lossy links (players), and strict access control (“no peeking at the DM notes”). This talk introduces **DungeonPy**, which evolves a Python D&D companion from two local app – a Pygame battle map and a PySimpleGUI initiative/condition tracker – connected by lightweight TCP messages, into an authoritative server with multiple role-aware clients. The result is a fully real-time interactive setup, where the DM controls the full state and can reveal information selectively – under the hood it’s all about client intents, server validation, state updates, event broadcasting and periodic snapshots. We will cover protocol design (deltas vs snapshots, ordering/idempotency), server-side view projections (DM omniscience vs per-player truth and fog-of-war), UI-safe concurrency, and testing your homemade message bus without summoning race conditions. Expect patterns you can reuse in any stateful client/server app – just with more goblins.</p>
|
|
| /talks/88TTRY/ |
<h3 class="talk-title">
Sentinel Values in Python: Semantics, Double Dispatch, and the Limits of Typing
</h3>
<h4>
Florian Wilhelm
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Programming & Software Engineering & Testing</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Intermediate</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Intermediate</span>
</div>
</div>
<p class="talk-abstract">Python relies heavily on special values such as `None`, `NotImplemented`, `Ellipsis`, and `dataclasses.MISSING`. These values are not incidental: they encode language semantics, enable control flow between objects, and shape API design.
This talk examines sentinel values as a first-class concept in Python. We will look at why None is often the wrong representation for absence, how NotImplemented enables double dispatch in rich comparisons, and where sentinel values appear throughout the standard library.
A central focus is typing. While sentinel values are ubiquitous at runtime, Python currently has no standardized way to express them precisely in type hints. We will examine why Optional, overloads, and Literal fall short, what limited narrowing is possible today, and why creating a “real” custom sentinel with reliable type narrowing is still unsolved.
Finally, we will discuss [PEP 661](https://peps.python.org/pep-0661/), i.e., the deferred proposal to standardize sentinel values and their typing semantics, and what its deferral means in practice. Using real-world examples, including Pydantic’s experimental missing concept, this talk provides a clear mental model for sentinel values and realistic guidance for using them in typed Python codebases today.</p>
|
|
| /talks/WDHTQR/ |
<h3 class="talk-title">
Simulating the World using SimPy: A practical Example
</h3>
<h4>
Niklas
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">PyData & Scientific Libraries Stack</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">None</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">None</span>
</div>
</div>
<p class="talk-abstract">Modern systems are complex - and testing them in real environments is often expensive, risky, or simply not reproducible. Simulation is a practical way to explore behavior under controlled conditions: run scenarios, validate assumptions, inject failures on purpose, and repeat experiments without touching production.
In this talk, I build a concrete event-based simulation with `SimPy` to compare `load-balancing algorithms` under different conditions. I’ll show how `SimPy`’s processes and events fit together, how to structure the simulation cleanly, and how to move beyond a one-off demo by making runs reproducible and configurable - using `configuration files` and a simple `command-line interface`.</p>
|
|
| /talks/99UMEL/ |
<h3 class="talk-title">
When Space Weather Breaks Your GPS: Building an Explainable Early Warning System
</h3>
<h4>
Vincenzo Ventriglia
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Machine Learning & Deep Learning & Statistics</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Intermediate</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Intermediate</span>
</div>
</div>
<p class="talk-abstract">Have you ever happened to use GPS and realised that it is not working properly? The Sun could be responsible.
In this talk, I present a **real-world machine learning forecasting system** designed to predict a Space Weather phenomenon affecting GNSS accuracy and radio communications. The system is based on **CatBoost** and integrates data from space- and ground-based observations. **SHAP** is used to debug model behaviour and to build trust in model outputs. The talk focuses on **model design and evaluation choices**, showing how interpretability and uncertainty-aware forecasting can be combined in a real-time operational pipeline.</p>
|
|
| /talks/RUSUYF/ |
<h3 class="talk-title">
7 Anti-Lessons from Building a PydanticAI Agent: Mistakes We Made So You Don't Have To
</h3>
<h4>
Joshua Görner
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Autonomous Systems & AI Agents</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Intermediate</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Intermediate</span>
</div>
</div>
<p class="talk-abstract">Life sciences compliance isn't forgiving. When your software helps companies navigate FDA regulations, ISO 13485, and EU MDR, "move fast and break things" isn't an option. Audit trails matter. Documentation is mandatory. Getting it wrong means regulatory findings, delayed product launches, or worse — patient safety risks.
During the development of our AI Assistant we made every mistake in the most unforgiving environment possible. After more than a year building with PydanticAI, pydantic-evals, and Claude — nearly 3,000 commits and 20+ contributors — here are 7 anti-lessons so you don't have to repeat them:
1. **"We need a multi-agent system"** — We built one. Then deleted it.
2. **"Agents need sophisticated planning"** — A todo list beat our workflow engine.
3. **"Give the agent lots of specific tools"** — Two high-level tools replaced dozens.
4. **"Encode workflows in code"** — Markdown files the agent reads at runtime won.
5. **"It works when I test it"** — Simple tests ≠ real user journeys. Realistic evals or you're blind.
6. **"Automate everything"** — Human stays in the driver's seat, not the trunk.
7. **"Apply what made you successful before"** — Your engineering instincts might hurt you here.
Real code, real git commits, real mistakes from a domain where mistakes are expensive.
**Come for the mistakes. Leave with shortcuts.**</p>
|
|
| /talks/N8QVT8/ |
<h3 class="talk-title">
Accelerate FastAPI Development with OpenAPI Generator
</h3>
<h4>
Dr. Evelyne Groen, Kateryna Budzyak
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Programming & Software Engineering & Testing</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Intermediate</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Intermediate</span>
</div>
</div>
<p class="talk-abstract">Develop FastAPI applications faster with the contract-first approach using the OpenAPI Generator, no GenAI required.
**To attend this workshop, please install the openapi generator.**
For details, please visit the README.md of https://gitlab.com/Eeffee/pycon26
Machine learning models are often deployed as APIs, but the "agreement" between the consumer and the service is often fragile. How does the consuming app know if a parameter is optional or required? When the code diverges from the documentation, integration breaks.
In this tutorial you will learn to define an API contract using OpenAPI specification. We will use the OpenAPI Generator to automatically generate API endpoints and strictly typed Pydantic data models. Following this approach for all applications supports standardization, consistency, and maintainability across all projects.
The session will cover three key areas:
**Design**: We will define an OpenAPI specification as our single source of truth for the API and end consumer.
**Generate**: We will use the OpenAPI Generator to create a FastAPI skeleton and show possibilities for customization to fit specific project needs.
**Implement**: We will connect our generated app to a ML model where we will create Mystic Creatures for Real Life Problems</p>
|
|
| /talks/EPASS8/ |
<h3 class="talk-title">
Building MCP at the Speed of Hype: Principles That Outlast the Trends
</h3>
<h4>
Rahkakavee Baskaran, Friederike Bauer
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Autonomous Systems & AI Agents</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Intermediate</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Intermediate</span>
</div>
</div>
<p class="talk-abstract">Every week, development in AI brings us another groundbreaking release, another model version, another must-have integration. In this rapidly shifting landscape, how does one build production systems that won’t be obsolete by the time you deploy them?
We'll explain how trusting in proven engineering principles from software development and machine learning, like separation of concerns and evaluation practices, became our anchor in an ever-changing landscape of AI development. We share lessons learned from building two MCP applications using FastMCP and PydanticAI. Against these challenges, we found that fundamental engineering principles provided the foundation we needed.
Participants in the process of developing AI tools will leave with practical strategies for building AI-powered systems that are flexible enough to adapt, yet stable enough to trust.</p>
|
|
| /talks/YKQ33N/ |
<h3 class="talk-title">
Destructive Testing: 10 Practical Ways to Expose Hidden Application Risks
</h3>
<h4>
Pascal Puchtler
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Programming & Software Engineering & Testing</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Novice</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Intermediate</span>
</div>
</div>
<p class="talk-abstract">Modern applications rarely fail in obvious ways. Instead, they break at the edges: unexpected inputs, race conditions, misused APIs, and assumptions nobody realized they were making. This talk presents ten practical and repeatable ways to intentionally break an application, using a QA mindset with a strong Python focus.
The session is designed to help QAs sharpen their investigative approach and move beyond happy-path testing, while giving developers concrete insight into where real-world failures often originate. Each “way to break an application” highlights a common risk area such as data handling, state management, timing, configuration, or integration boundaries.
Attendees will learn how to think more destructively (in a productive way), design better tests, and recognize fragile design decisions earlier. The goal is not to assign blame, but to improve collaboration and software quality by understanding how systems actually fail in practice.</p>
|
|
| /talks/MS7AWK/ |
<h3 class="talk-title">
Escape the Hype: Teaching LLM Concepts Through an Interactive AI Factory Game
</h3>
<h4>
Vadim Vlasov, Eric Glaser, Lisa Amrhein
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Education, Career & Life</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">None</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Novice</span>
</div>
</div>
<p class="talk-abstract">Everyone talks about LLMs, RAG, and AI agents - but who truly understands them? Marketing promises magic while documentation assumes expertise. Recent research from Gartner reveals the consequences: only 8% of HR leaders believe their managers possess adequate AI competency, while companies that restructure work around AI achieve revenue goals twice as often as those who merely train employees. The problem isn't lack of information; it's the lack of genuine understanding through experience.
We took a different approach. Instead of slides or tutorials, we built "AI Factory" - a non-profit educational platform in the form of escape room game where players learn by doing. Craft prompts under budget pressure. Watch guardrails fail in real-time. Break their own RAG pipeline. Each mistake teaches more than any documentation ever could.
In this talk, we'll share what we discovered while building and testing this game with real users: why failure-driven learning outperforms tutorials, how game mechanics create memorable "aha moments," and the surprising concepts that clicked only through play.</p>
|
|
| /talks/PFXR9G/ |
<h3 class="talk-title">
Fight your garbage data: implementation of a pythonic data quality monitoring framework in PySpark
</h3>
<h4>
Rostislaw Krassow, Joshua Finger
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Data Handling & Data Engineering</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Intermediate</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Intermediate</span>
</div>
</div>
<p class="talk-abstract">The timeless phrase “garbage in, garbage out” is even more important today with the growing usage of non-deterministic generative neuronal networks, which amplifies the effect of bad data quality. This presentation describes Data Quality Monitor — a tool to bring transparency into data quality and help drive real improvements.
In the talk, we'll cover what defines a successful data quality monitoring solution and share findings from our initial evaluation of available open-source frameworks. Next, we'll showcase our implementation based on DQX. DQX is a lightweight, open-source framework for performing row-level data quality checks programmatically, with business rules organized in manageable YAML files. DQX, originally developed by Databricks Labs, integrates seamlessly with PySpark, making it easy and affordable to run data quality checks within our IoT data lake. Finally, we will discuss the organizational processes and structures required to effectively respond to data quality issues.</p>
|
|
| /talks/39MHWT/ |
<h3 class="talk-title">
From Ticket to Draft: How Munich Automates Citizen Inquiries with AI
</h3>
<h4>
Leon Lukas
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Natural Language Processing & Audio (incl. Generative AI NLP)</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Novice</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Intermediate</span>
</div>
</div>
<p class="talk-abstract">The City of Munich is modernizing its communication: With the transition to the Zammad ticketing system, there is a unique opportunity to not only manage citizen inquiries but to proactively process them using Artificial Intelligence. The Zammad-AI project utilizes a two-stage process consisting of intelligent classification and RAG-based (Retrieval-Augmented Generation) response drafting to significantly reduce the workload of administrative staff.
In this talk, we demonstrate how we integrated Zammad-AI via an internal Kafka message bus to process tickets in real-time. We explore the technical workflow—from thematic context analysis to the generation of valid response drafts based on a department-specific knowledge base.</p>
|
|
| /talks/TB9WYZ/ |
<h3 class="talk-title">
How to compare apples with oranges: Proper evaluation of article-level demand forecasts
</h3>
<h4>
Stefan Birr, Mones Raslan
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Machine Learning & Deep Learning & Statistics</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Novice</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Intermediate</span>
</div>
</div>
<p class="talk-abstract">How do you evaluate performance when you predict more than 10 million time series each day? While a good plot can be worth more than a thousand metrics for a single time series, with large-scale machine learning models implemented with *LightGBM* and *PyTorch* we have to resort to meaningful aggregations. We will share insights and learnings from the past 2 years of deploying and operating our article-level demand forecasting models at the pricing department of Zalando.
This talk moves beyond basic metrics to showcase the pitfalls of aggregated error measures and the best practices we’ve developed to keep our stakeholders informed and our models accurate.</p>
|
|
| /talks/3C9P9V/ |
<h3 class="talk-title">
Is my AI Recruiting biased? - How to evaluate these systems
</h3>
<h4>
Sebastian Krauss
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Ethics & Privacy</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Novice</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Intermediate</span>
</div>
</div>
<p class="talk-abstract">AI recruiting systems are increasingly used to filter, rank, and select applicants at scale. Yet their deployment raises essential questions: How reliable are these models in real hiring environments, and how do we ensure fairness and safety across diverse applicant profiles? This talk presents a structured approach to testing and validating AI-driven recruiting pipelines. It highlights the role of synthetic test data, data augmentation, and fairness metrics in uncovering systemic risks and mitigating bias. Attendees will walk through a complete evaluation workflow. The session also incorporates insights from real-world testing practices, demonstrating how rigorous validation can increase trust and transparency in recruitment AI.</p>
|
|
| /talks/VUHSG9/ |
<h3 class="talk-title">
Letting AI Move: Robotics Demos Powered by Python
</h3>
<h4>
Larissa Haas, Annika Herbert
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Embedded Systems & Robotics</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Novice</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Novice</span>
</div>
</div>
<p class="talk-abstract">AI is sometimes hard to explain, especially for people outside of tech. With robots, AI becomes visible and tangible. In this talk we want to show how we can use Python and the huggingface reachy mini as an example to make AI more concrete, interactive, and engaging for beginners and non-experts.</p>
|
|
| /talks/8YTYEN/ |
<h3 class="talk-title">
Octopus AutoML: Extracting Signal from Small and High-Dimensional Data
</h3>
<h4>
Nils Haase, Andreas Wurl
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Machine Learning & Deep Learning & Statistics</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Intermediate</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Intermediate</span>
</div>
</div>
<p class="talk-abstract">Many machine learning tools assume abundant, independent data, rely on a single data split plus cross-validation, and leave test-set separation to the user.
In application-driven domains such as industrial materials science and pharmaceutical development, data are scarce, high-dimensional, and often correlated, creating conditions under which standard ML pipelines frequently fail. Small datasets are highly sensitive to the random seed used for splitting, and common pitfalls such as feature selection before splitting or distributing correlated samples across train and test sets cause data leakage and inflated performance metrics.
Octopus is an open-source Python AutoML library explicitly designed for small-data, high-dimensional regime. It enforces strict nested cross-validation for model and hyperparameter selection, quantifies performance variability across multiple splits, and tightly controls data leakage. Its modular architecture embeds an internal ML engine, several feature selection methods (e.g., MRMR, Boruta), and external AutoML solutions such as AutoGluon into a unified, rigorous validation framework, enabling systematic and fair comparison of methods on limited data. In addition, Octopus supports survival analysis, addressing time-to-event problems common in healthcare and materials science. This talk will use realistic small-scale datasets to illustrate how conventional pipelines can be misleading and how to obtain more reliable models when every sample matters.</p>
|
|
| /talks/HKFCBM/ |
<h3 class="talk-title">
Pair & Share: How formal Mentoring pushed REWE Analytics to a new level
</h3>
<h4>
Axel Buddendiek
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Education, Career & Life</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">None</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Novice</span>
</div>
</div>
<p class="talk-abstract">As one of Europe’s largest retail corporations, REWE Group owns and manages prominent supermarket chains such as REWE and PENNY, among many other subsidiaries. In this talk I will give a brief overview of how we introduced a formal mentoring program, Pair & Share, at the central analytics department of REWE Group with its more than 150 data scientists, engineers, analysts and other colleagues.
Before Pair and Share, there was no formal process for personal, technical or methodological growth. Although there are plenty of possibilities, further training and education was self-organized and fragmented. To increase growth among our colleagues and build and strengthen inter-team exchange, we introduced the formal mentoring program, Pair & Share.
This talk will cover a brief overview of REWE Group and our analytics department followed by a motivation for Pair & Share. Afterwards I will explain how we planned the mentoring program and defined the parameters like the matching process, the time frame and how to recruit participants. I will also share my experiences of the first six months of mentoring, what kind of roadblocks but also pleasant surprises we encountered. The talk will be concluded with an outline of how we plan to continue and improve the program.</p>
|
|
| /talks/DVCKHF/ |
<h3 class="talk-title">
Personalized Restaurant Recommendations at Scale combining Transformer with Gradient-Boosted Ranking
</h3>
<h4>
Marcel Kurovski, Steffen Klempau
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Machine Learning & Deep Learning & Statistics</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Intermediate</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Advanced</span>
</div>
</div>
<p class="talk-abstract">Wolt’s Universal Venue Ranker (UVR) is a large-scale, sequence-aware ranking model for personalized restaurant recommendations, deployed across more than 30 countries. UVR replaces three previously independent models—Neural Collaborative Filtering, a second-pass ranker, and a first-time-user model—by combining a transformer with a gradient-boosted decision tree for ranking.
The model follows a two-stage design. In the first stage, an encoder-style transformer learns a personalized user state representation from historical restaurant purchase sequences enriched with spatiotemporal signals such as time and location. In the second stage, a CatBoostRanker uses the transformer output as an input feature alongside additional user-, venue-, user–venue-, and delivery-specific features to score and rank candidate venues.
In this talk, we present the model and service architecture, the training and evaluation setup, and both offline and online results from a multi-country online A/B test, demonstrating significant improvements in global conversion rate and new venue trial rate. We also share practical lessons from deploying and operating a multi-stage ranking model under strict latency constraints at global scale.</p>
|
|
| /talks/Q9HMT3/ |
<h3 class="talk-title">
Schema-Driven Lambdaliths in Python with AWS Lambda Powertools and Pydantic
</h3>
<h4>
Tanio Toranosuke, Haruto Mori
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Django & Web</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Intermediate</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Intermediate</span>
</div>
</div>
<p class="talk-abstract">Modern web frameworks such as Hono have renewed interest in schema-driven development and the “Lambdalith” architecture, where an application is delivered as a single AWS Lambda function. While this model provides a predictable developer experience, Python-based serverless systems often struggle to achieve the same consistency, validation, and maintainability in production.
Deploying Python web frameworks to AWS Lambda frequently requires additional execution layers—such as ASGI adapters or container-based runtimes—which add complexity and blur data boundaries. For teams that prefer clear, minimal Lambda handlers, these abstractions can hinder both development and operations.
This session shares production-proven patterns for building schema-driven Lambdalith applications in Python using AWS Lambda Powertools and Pydantic, without relying on heavy framework abstractions. Through real-world examples, we show how these tools simplify handler logic, standardize request and response validation, and improve observability and error handling.
Attendees will leave with practical techniques for building reliable and maintainable Python Lambdalith systems, and insights they can immediately apply to modernizing existing serverless codebases or delivering new production services with confidence.</p>
|
|
| /talks/AZ7GD3/ |
<h3 class="talk-title">
Small Language Models for Tool Calling Are Better Than You Think
</h3>
<h4>
Gabi Kadlecova
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Natural Language Processing & Audio (incl. Generative AI NLP)</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Intermediate</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Intermediate</span>
</div>
</div>
<p class="talk-abstract">Large language models have been widely used in tool-calling workflows thanks to their strong performance in generating appropriate function calls. However, due to their size and cost, they are inaccessible to small-scale builders, and server-side computing makes data privacy challenging. Small language models (SLMs) are a promising, affordable alternative that can run on local hardware, ensuring higher privacy.
Unfortunately, SLMs struggle with this task - they pass wrong arguments when calling functions with many parameters, and make mistakes when the conversation spans multiple turns. On the other hand, for production applications with specific API sets, we often don't need general-purpose LLMs - we need reliable, specialized models.
This talk demonstrates how to increase the accuracy of SLMs (under 8B parameters) for custom tool calling tasks. We will share how leveraging knowledge distillation helps to get the most out of SLMs in low-data settings - they can even outperform LLMs! We will present the whole pipeline from data generation, fine-tuning, and local deployment.</p>
|
|
| /talks/GAUNKM/ |
<h3 class="talk-title">
When LLMs Are Too Big: Building Cost-Efficient High-Throughput ML Systems for E-Commerce Cataloging
</h3>
<h4>
Tobias Senst, Bastian Wandt
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">MLOps & DevOps</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Intermediate</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Advanced</span>
</div>
</div>
<p class="talk-abstract">E-commerce cataloging at idealo operates at extreme scale: 4.5 billion offers from 50,000+ shops across six countries, with peak ingestion rates of 4.8 million offers per minute. While large language models (LLMs) provide strong classification accuracy, they are too slow and costly for billion-scale real-time processing. This talk shows how idealo builds a cost-efficient, high-throughput machine learning system that leverages LLM knowledge without deploying full models in production.
We present how knowledge distillation from a large e5 instruction model enables a compact multilingual MiniLM encoder to achieve high accuracy, and how optimized inference runtimes and specialized hardware such as AWS Neuron help meet strict latency and cost requirements. Beyond modeling, we highlight key operational challenges: constructing training datasets from massively imbalanced data, selecting the right encoder architecture from today’s model landscape, and designing a robust MLOps lifecycle with automated data sampling, training, deployment, and monitoring.
Attendees will learn practical techniques for scaling ML systems under real-world constraints, how to extract value from LLMs when they are too large to serve directly, and how to transition research prototypes into reliable, high-volume production pipelines.</p>
|
|
| /talks/TPNBRN/ |
<h3 class="talk-title">
Zero-Copy or Zero-Speed? The hidden overhead of PySpark, Arrow & SynapseML for inference
</h3>
<h4>
Petar Ilijevski
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Data Handling & Data Engineering</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Advanced</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Intermediate</span>
</div>
</div>
<p class="talk-abstract">"Zero-copy" data transfer promises free communication between Spark's JVM and Python workers, but at 6 billion rows daily, the reality is far more complex. This session explores the low-level mechanics of distributed inference, focusing on the serialization bottlenecks.
We will conduct an analysis of execution plans generated by `pandas_udf`, `mapInPandas`, and SynapseML. We visualize the true cost of pickling, Arrow record batching, and JNI context switching. Join this deep dive to understand the physics of distributed inference and learn how to tune `spark.sql.execution.arrow.maxRecordsPerBatch` to prevent OOMs without starving the CPU.</p>
|
|
| /talks/B8KVNJ/ |
<h3 class="talk-title">
Accuracy Is Overrated: Ship Stable Forecasts (Without Lying to Yourself)
</h3>
<h4>
Illia Babounikau
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Machine Learning & Deep Learning & Statistics</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Novice</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Intermediate</span>
</div>
</div>
<p class="talk-abstract">Forecasting talks love a clean ending: “and then we improved WMAPE by 3.7%.”
Nice. Now put that model into production without suffering from instability.
You retrain your model on a few new weeks of data and suddenly the one-year forecast jumps 15–20%. Planning teams redo decisions, trust erodes, and your “accurate” model becomes unusable. This talk is about forecast stability: how much forecasts change when you add new data and rerun the same pipeline.
We run a simple experiment: train a model, forecast one year ahead, add recent data, retrain, and measure forecast-to-forecast change. We repeat this across common forecasting approaches including ETS/ARIMA, Prophet, XGBoost with lag features, AutoGluon ensembles, neural/global models, and TimeGPT-style APIs.
You will see that high accuracy does not guarantee usable forecasts, and that some models are systematically more volatile than others. We then cover practical ways to stabilise forecasts without freezing them, focusing on reconciliation and ensembling (including origin ensembling).
This talk is for forecasting practitioners who want models users actually trust, not just good metrics.</p>
|
|
| /talks/U9KQU9/ |
<h3 class="talk-title">
Building Trust in Your Data Pipelines with Observability
</h3>
<h4>
Stefan Dienst
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Data Handling & Data Engineering</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">None</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Intermediate</span>
</div>
</div>
<p class="talk-abstract">In the daily work of a data engineer, building new data pipelines often takes priority, while maintaining them and ensuring their correctness becomes an afterthought. This focus can quickly turn into a pitfall: failures go undetected, incorrect data silently propagates, and complaints from stakeholders arrive before engineers notice any issues. In practice, incorporating observability into every new data pipeline helps avoid these problems and enables teams to steadily increase system complexity while maintaining trust and peace of mind.
In this talk, I introduce observability in the context of data pipelines, covering its three core pillars: metrics, alarms, and logs. We will explore concepts like the four golden signals, alarm fatigue and structured logging and how they apply to data pipelines. I will show easy to implement first steps and share real-world experiences, where improved observability helped uncover previously unknown incorrect behavior and build trust in data systems.
This talk is well suited for data engineers that had little exposure to observability and want to learn about strategies how to keep sane while managing a jungle of pipelines.</p>
|
|
| /talks/BLC7FS/ |
<h3 class="talk-title">
Demystifying Containers with Python: Building a Minimal Engine from Scratch
</h3>
<h4>
Alexander Zaytsev
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Programming & Software Engineering & Testing</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Novice</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Novice</span>
</div>
</div>
<p class="talk-abstract">Containers are a fundamental part of the modern developer's toolkit, yet they are frequently misunderstood and described as "lightweight virtual machines." This talk demystifies containerization by building a functional, minimal engine from scratch using only the python standard library. We will step away from high-level tools like docker to explore how the linux kernel provides isolation through features like `namespaces` and `chroot`. Using a hands-on approach, we will demonstrate how to set up a sandboxed environment, isolate a filesystem, and execute processes within it. This session is designed for developers who use containers daily but haven't yet had the opportunity to look under the hood or explore the underlying operating system principles. By implementing a simplified version of these tools, you will gain a clearer, more practical understanding of the core mechanics that make containerization possible.</p>
|
|
| /talks/9MUDUY/ |
<h3 class="talk-title">
Empowering Data Scientists with Zero Platform Friction: Deploying Streamlit & Friends in 3 Minutes
</h3>
<h4>
Bernhard Schäfer, Nicolas Renkamp
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">MLOps & DevOps</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Intermediate</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Intermediate</span>
</div>
</div>
<p class="talk-abstract">A data scientist builds a Streamlit or Dash prototype, the business wants to validate it, and the hard parts begin: getting access to live data, making the app available company-wide, and ensuring every user only sees what they are allowed to see. Following "best practices" turn a simple demo into weeks of platform work, leaving data scientists frustrated and blocking them from shipping apps to end users.
In this talk we will **live-demo** Merck's self-service app service we have developed and hardened over multiple years. It lets **teams deploy Streamlit (and friends) in 3 minutes** while meeting best practices like SSO, CI/CD, and governed data access control. The platform has become essential for Merck to ship data apps at scale: in 2025 it powered **750+ active apps** reaching **8,000+ unique end users**.
**Under the hood, we show:** how a use-case based access model enables scoped resource permissions so apps can safely access data on-behalf of the user. We also show starter templates that generate a deployable Git repo with example pages (e.g. Snowflake access or internal LLM chatbot). Finally, we cover the guardrails needed to operate this safely.
**What you will learn:** a cost-effective reference architecture based on AWS that you can adapt to your hyperscaler or platform, practical patterns for balancing the trade-off between central control and decentral freedom, and how templates and CI/CD help teams iterate quickly without compromising security or reliability.</p>
|
|
| /talks/GPV9SM/ |
<h3 class="talk-title">
Holistic Optimization: Implementing "Pipeline-as-a-Trial" HPO with Ray and Cloud Infra
</h3>
<h4>
Abdullah Taha
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">MLOps & DevOps</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Intermediate</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Intermediate</span>
</div>
</div>
<p class="talk-abstract">Most hyperparameter optimization (HPO) stops at the model boundary. But what happens when your system relies on a complex chain of steps, a short-horizon model, a long-horizon model, ensembles, postprocesses etc? Tuning one piece in isolation often leads to sub-optimal global results.
In this talk, we explore how we used Ray to move beyond simple model tuning. We’ll dive into a "Pipeline-as-a-Trial" architecture where Ray acts as the brain, triggering independent, scalable cloud workflows ( SageMaker Pipelines or Databricks Workflows) for every hyperparameter set.
We will discuss:
* The architectural shift from tuning models to tuning pipelines
* How to build the DAG/pipeline on Sagemaker/Databricks using declarative configs
* How to use Ray to orchestrate heavyweight remote jobs without bottlenecks.
Attendees will learn how to optimize entire pipelines (in a scalable manner on cloud) to minimize global metrics like WAPE, rather than just local model loss.</p>
|
|
| /talks/P7NYXB/ |
<h3 class="talk-title">
Tracking Knowledge Diversity in LLM-Generated Responses.
</h3>
<h4>
Sarah Masud
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Natural Language Processing & Audio (incl. Generative AI NLP)</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Intermediate</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Intermediate</span>
</div>
</div>
<p class="talk-abstract">As large language models (LLMs)-powered “AI highlights” become the first information people see on the Web, a key question arises: how much variety and perspective do these systems actually deliver for information-seeking queries? Do LLMs offer broader viewpoints than traditional search or Wikipedia pages? Do larger models really produce more diverse answers—or are they all converging on the same language, and framing, raising concerns about “knowledge collapse”?
Drawing insights from experiments across LLM families, real-world topics, and hundreds of user-style prompts, this talk introduces an open-source framework for benchmarking and tracking epistemic diversity in LLMs. We focus on practical lessons for data scientists building and evaluating LLM-powered search, summaries, and knowledge systems—where diversity of information actually matters.</p>
|
|
| /talks/X3KQMQ/ |
<h3 class="talk-title">
Before You Ship Your Agent: An Agent Builder’s Primer on Jailbreaking Attacks
</h3>
<h4>
Simonas Černiauskas
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Autonomous Systems & AI Agents</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Intermediate</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Novice</span>
</div>
</div>
<p class="talk-abstract">Before you ship an AI agent to production, you need to understand how it can be broken. Jailbreaking and prompt injection attacks are not edge cases—they are an inevitable consequence of deploying real-world, action-taking AI systems.
This talk is a practical primer on the most common ways agents fail under adversarial pressure. We’ll break down how jailbreaking and prompt injection attacks actually work, including techniques such as excessive agency, prompt leakage, and weaknesses in vector search and embeddings. We’ll examine why popular AI guardrails consistently fail in practice, and offer little more than a false sense of protection.
We’ll also address a common misconception: the absence of major AI security incidents does not mean systems are safe. Instead, it reflects limited deployment, constrained agency, and cautious rollout. As organizations adopt browser agents, autonomous tools, and systems that can take real-world actions, these vulnerabilities quickly become critical attack surfaces.
This talk focuses on what organizations should do instead: applying proven security principles—least privilege, isolation, monitoring, and abuse modeling—adapted to the unique properties of AI systems. Attendees will leave with a clear understanding of the real risks, why they matter today, and the concrete steps to take before shipping an AI agent into production.</p>
|
|
| /talks/BFYYQG/ |
<h3 class="talk-title">
Build a web coding platform with Python, run in WebAssembly
</h3>
<h4>
Maris Nieuwenhuis
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Python Language & Ecosystem</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Novice</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Novice</span>
</div>
</div>
<p class="talk-abstract">Ever wanted to build a website that can run python, but you're worried about running user submitted code on your server?
In this talk I'll show how Holoviz Panel can create an interactive coding environment where students can write functions, solve exercises, and experiment safely, all while their code runs locally via WebAssembly.</p>
|
|
| /talks/EWZMJK/ |
<h3 class="talk-title">
Don’t call your LLM too often! How to build your dialog graph with confidence and sleep at night.
</h3>
<h4>
Evgeniya Ovchinnikova, Andrei Beliankou
</h4>
<div class="talk-info-container">
<div class="track-container">
<span class="track">Natural Language Processing & Audio (incl. Generative AI NLP)</span>
</div>
<div class="python-skill-container">
<span class="python-skill-label talk-info-label">Python Skill</span>
<span class="levels">Novice</span>
</div>
<div class="domain-expertise">
<span class="domain-expertise-label talk-info-label">Domain Expertise</span>
<span class="levels">Novice</span>
</div>
</div>
<p class="talk-abstract">Keywords: **Explainable AI, enhanced RAG, GraphRAG, LLMOps, dialog system evaluation.**
Designing reliable dialog flows for LLM-based systems remains challenging once conversations require branching, correction, or multi-step reasoning. Dialog graphs often evolve organically and accumulate structural issues: endless correction loops, dead subpaths, redundant validation steps, overly generic catch-all branches, or linear sequences that should be collapsed. Such phenomena raise operational costs, significantly increase TTFT and make the system answer less predictable and explainable.
Many solutions try to introduce an all-fit generalized RAG retrieval solution. Contrary to this, we present our empirical learnings on how to enhance system speed, lower overall costs and offer a better dialog graph explainability through enhanced LLM call tracing and iterative enhancements for common dialog paths.
We also show that more elaborated knowledge retrieval strategies like GraphRAG may drastically enhance overall response quality and shorten the dialog graph. We evaluate several approaches and give recommendations on how to leverage more complex document indexing phases for inference time benefits.
Overall, the session argues that scalable conversational systems require not only better prompts, but explicit graph structures paired with rigorous tracing and data-driven optimization.</p>
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|
Внешние ссылки
Кол-во: 8
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| Url | Анкор |
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| pretalx.com |
Schedule
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| pretalx.com |
Schedule
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| pretalx.com |
Schedule
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| 2025.pycon.de |
2025
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| 2024.pycon.de |
2024
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| 2023.pycon.de |
2023
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| 2022.pycon.de |
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| 2019.pycon.de |
2019
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