10 Years of Automated Category Classification for Product Data
Johannes Knopp
Artificial Intelligence, Deep Learning, Data Science, Infrastructure, Machine Learning, Data Engineering

10 years ago we built a classifier for categorizing product data. Let's take a journey through the lessons we learned over the years about building, maintaining, and modernizing the category classifier.

6 Years of Docker: The Good, the Bad and Python Packaging
Sebastian Neubauer
DevOps, Infrastructure, IDEs/ Jupyter, Use Cases

In this talk I will walk you through the proper setup of a local python development environment using docker.

Airflow: your ally for automating machine learning and data pipelines
Enrica Pasqua, Bahadir Uyarer
Big Data, Infrastructure, Machine Learning, Data Engineering

Automate your machine learning and data pipelines with Apache Airflow

Boosting simulation performance with Python
Eran Friedman
Infrastructure, Robotics

Simulating hours of robots' operation in minutes with Python

Docker and Python - A Match made in Heaven
Dr. Hendrik Niemeyer
Big Data, DevOps, Infrastructure

Learn how to build and ship Python software with Docker Containers.

Fighting fraud: finding duplicates at scale
Alexey Grigorev
Data Science, Infrastructure, Machine Learning, Data Engineering

Fight fraudsters at scale: use machine learning to find duplicates in 10 million ads daily

Is it me, or the GIL?
Christoph Heer
Infrastructure, Parallel Programming, Visualisation

People often complain about the GIL, but does your application actually suffer from the GIL?

Julia for Python
Simon Danisch
Data Science, Infrastructure, IDEs/ Jupyter, Parallel Programming

Julia is a new Language, that is fast, high level, dynamic and optimized for Data Science. Learn about Julia's strengths and how you can integrate it in your Python workflow!

Kubernetes 101 for Python Developers
Christian Barra
DevOps, Infrastructure, Web, APIs, Use Cases

Ready to learn about Kubernetes? Join the workshop and be prepared to play with yaml files!

Managing the end-to-end machine learning lifecycle with MLFlow
Tobias Sterbak
Data Science, Infrastructure, Machine Learning, Data Engineering

How to manage the end-to-end machine learning lifecycle with MLflow.

Monitoring infrastructure and application using Django, Sensu and Celery.
Hari Kishore Sirivella
Django, DevOps, Infrastructure

Monitoring infrastructure and application using Django, Sensu and Celery.

Package and Dependency Management with Poetry
Steph Samson
DevOps, Infrastructure, Use Cases

Learn how to make package and dependency management easier with Poetry.

Parallel programming for python developers – Let’s Go(lang)
Dominik Henter, Jéssica Lins
Infrastructure, Networks, Parallel Programming

A tutorial about parallel programming in Go, from the perspective of a Python developer.

pytest - simple, rapid and fun testing with Python
Florian Bruhin

The pytest tool presents a rapid and simple way to write tests for your Python code. This training gives an introduction with exercises to some distinguishing features.

Quantum computing with Python
James Wootton
Algorithms, Infrastructure, Microcontrollers, Science, APIs

Every Python user can play with one of the world's most advanced technologies: quantum computers. This session will tell you how you can and why you should.

Tools that help you get your experiments under control
Katharina Rasch
Artificial Intelligence, Data Science, DevOps, Infrastructure

There is now a wealth of tools that support data science best practices (e.g. tracking experiments, versioning data). Let’s take a look at which tools are available and which ones might be right for your project.

What we learned from scraping 1 billion webpages every month
Samet Atdag
Business & Start-Ups, Big Data, Infrastructure, Web, Data Engineering

We broke the web via simple hacks. Instead of order, we caused chaos. How to fix that?

🌈Apache Airflow for beginners
Infrastructure, Data Engineering

Airflow can sound more complicated than it is. Learn the basics on the practical example.