Johannes KnoppArtificial 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.
Sebastian NeubauerDevOps, Infrastructure, IDEs/ Jupyter, Use Cases
In this talk I will walk you through the proper setup of a local python development environment using docker.
Enrica Pasqua, Bahadir UyarerBig Data, Infrastructure, Machine Learning, Data Engineering
Automate your machine learning and data pipelines with Apache Airflow
Eran FriedmanInfrastructure, Robotics
Simulating hours of robots' operation in minutes with Python
Dr. Hendrik NiemeyerBig Data, DevOps, Infrastructure
Learn how to build and ship Python software with Docker Containers.
Alexey GrigorevData Science, Infrastructure, Machine Learning, Data Engineering
Fight fraudsters at scale: use machine learning to find duplicates in 10 million ads daily
Christoph HeerInfrastructure, Parallel Programming, Visualisation
People often complain about the GIL, but does your application actually suffer from the GIL?
Simon DanischData 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!
Christian BarraDevOps, Infrastructure, Web, APIs, Use Cases
Ready to learn about Kubernetes? Join the workshop and be prepared to play with yaml files!
Tobias SterbakData Science, Infrastructure, Machine Learning, Data Engineering
How to manage the end-to-end machine learning lifecycle with MLflow.
Hari Kishore SirivellaDjango, DevOps, Infrastructure
Monitoring infrastructure and application using Django, Sensu and Celery.
Steph SamsonDevOps, Infrastructure, Use Cases
Learn how to make package and dependency management easier with Poetry.
Dominik Henter, Jéssica LinsInfrastructure, Networks, Parallel Programming
A tutorial about parallel programming in Go, from the perspective of a Python developer.
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.
James WoottonAlgorithms, 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.
Katharina RaschArtificial 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.
Samet AtdagBusiness & 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?
VaryaInfrastructure, Data Engineering
Airflow can sound more complicated than it is. Learn the basics on the practical example.