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.

Build a Machine Learning pipeline with Jupyter and Azure
Daniel Heinze
Computer Vision, Deep Learning, DevOps, IDEs/ Jupyter, Machine Learning, APIs, Python

Build a Machine Learning pipeline with Jupyter and Azure: https://notebooks.azure.com/Starlord/projects/pycon-ml-jupyter-azure

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.

Introduction to automated testing with pytest
Raphael Pierzina
DevOps, Web, Data Engineering

Learn how to get started with developing automated tests in Python with the pytest test framework!

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!

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.

Practical DevOps for the busy data scientist
Dr. Tania Allard
Algorithms, Big Data, Data Science, DevOps, Machine Learning

Devops for the busy data scientist: learn how to leverage these practices to improve your workflows

Production-level data pipelines that make everyone happy using Kedro
Yetunde Dada
Data Science, DevOps, Machine Learning, Data Engineering

Learn how easy it is to apply software engineering principles to your data science and data engineering code. Expect an overview of Kedro, a library that implements best practices for data pipelines with an eye towards productionizing ML models.

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.

venv, pyenv, pypi, pip, pipenv, pyWTF?
Simone Robutti
Code-Review, DevOps

Are you confused about the difference between pyenv and pipenv? Or between pip and Pypi? We will talk about them and many other Python tools