5 Things You Want to Know About AI Adoption in the Enterprise
Alexander CS Hendorf
Architecture, Best Practice, Business & Start-Ups, Corporate, Diversity & Inclusion

All one needs is strategy, skill and resources to make digitalization and AI happen. So why is everything taking so long? 5 Things You Want to Know About AI Adoption in the Enterprise.

A data scientist's guide to code reviews
Alexandra Wörner
Coding / Code-Review

Code reviews apply to all data science work - you sometimes just need to tweak them a bit. Let me show you when and how as well as what makes a fruitful code review.

A Smooth Ride: Online Car Buying and Selling at
Ricardo Kawase, Marlene Hense
Best Practice, Career & Freelancing, Use Case

Buying or selling a car is a challenging task that requires a lot of difficult decision-making. We will reveal all the "under the hood" data products at that support users in making the right decisions.

Biases in Language Models
Diversity & Inclusion, Ethics (Privacy, Fairness,… ), Natural Language Processing

Study of gender biases in popular language models and debiasing model techniques

Career Panel
Katharine Jarmul, Matteo Guzzo, Sieer Angar, Marielle Dado, Emily Gorcenski
Career & Freelancing

Are you thinking about a career change? In our career panel we will discuss different aspects with participants from different fields.

Come as you are: Transitioning from Science to Data Science
Dr. Hannah Bohle
Career & Freelancing

Come as you are: Transitioning from Science to Data Science. How to find your first job in industry after leaving academia.

conda-forge: supporting the growth of the volunteer-driven, community-based packaging project
Wolf Vollprecht, Jannis Leidel, Jaime Rodríguez-Guerra
Community, Packaging, Python - PyPy, Cython, Anaconda

How does the conda-forge packaging community work, what is its relationship to conda and PyPI and how can everyone package software with it?

Do I need to be Dr. Frankenstein to create real-ish synthetic data?
Data Engineering, Ethics (Privacy, Fairness,… ), Governance

Synthetic data not only address the privacy needs but also offer workaround for unprecedented situations. This talk introduces their different types, the options for their generation, and how you don't need to be a mad scientist to make realistic synthetic data

Do we really need Data Scientists?
Dr. Setareh Sadjadi
Career & Freelancing, Community

Is Data Science really cooling down? Do we need Data Scientists? What for?

Forget ‘web 3.0’, let's talk about ‘web 0.0’. A brief history of the Internet, and the World Wide Web.
Dom Weldon
Art, Social Sciences, Theory

Forget ‘web 3.0’, let's talk about ‘web 0.0’. A brief history of the Internet, and the World Wide Web.

How to build a Python-based Research Cloud Platform from scratch
Andre Fröhlich
Architecture, Business & Start-Ups, Use Case

This talk will present the journey of a quantitative asset manager from an outdated (non-Python) onPrem research setup to a modern Python-centric cloud research platform. We will examine the requirements and challenges associated with the project and present how we navigated find

How to deal with toxic people
Gina Häußge
Best Practice, Community

As an open source maintainer, sooner or later you'll encounter ungrateful, entitled or outright toxic people who can be a real drain on your motivation and general mental health. Here are some coping strategies that work for me!

Impact of Cultivating a Diverse and Inclusive Workplace
Riya Bansal
Community, Diversity & Inclusion

Let’s face it. The positive impact of diversity and inclusion is no longer debatable.

Introduction to MLOps with MLflow
Tobias Sterbak
Best Practice, Predictive Modelling, Reproducibility

Learn the basics of MLops with MLflow to manage the machine learning life-cycle.

Making Machine Learning Applications Fast and Simple with ONNX
Jan-Benedikt Jagusch, Christian Bourjau
Data Engineering, DevOps, Packaging

In this session, you will learn how to use ONNX for your machine learning model deployments, which can reduce your single-row inference time by up to 99% while also drastically simplifying your model management.

ML Communication 101: How to talk about Machine Learning with anyone
Julia Ostheimer
Best Practice, Business & Start-Ups, Career & Freelancing, Corporate, Diversity & Inclusion, Ethics (Privacy, Fairness,… ), Transparency / Interpretability, Use Case

You wanna know how you can explain your grandparents what #MachineLearning is? Attend the #PyConDE #PyData tutorial on how to translate #ML terms into everyday language of any audience. #communication #101 #tutorial #softskills #AI

Python for Everyone - PyLadies' Insights Panel Discussion
Jessica Greene (she/her)
Community, Diversity & Inclusion

Join this panel to learn more about how PyLadies volunteers and organizers make a difference, what they would like the wider python community to understand, so they could be more effective in their work, and what you could do tomorrow, to help advance this work.

Rewriting your R analysis code in Python
Helena Schmidt
Best Practice, Development Methods, R

R and Python are two of the most powerful tools for any kind of data analysis. But both programming languages have their strengths and weaknesses. This leads to the question: When and how to rewrite your R analysis code in Python?

Slack bots 101: An introduction into slack bot-based workflow automation
Jordi Smit
APIs, DevOps, Use Case

Most developers work with Slack every day, yet very few of them know about the awesome things you can do when you build your own slack bot. During this talk, we will teach you to build and deploy your first slack bot.

The Myth of Neutrality: How AI is widening social divides
Stefanie Stoppel
Ethics (Privacy, Fairness,… ), Neural Networks / Deep Learning

AI is not neutral and its creation often perpetuates harmful biases. My talk highlights how difficult it is to build "fair and responsible" AI, but also why it's worth to try & prevent these algorithms from cementing existing injustices.

Unclear Code Hurts
Dario Cannone
Best Practice, Coding / Code-Review

Code may work or not, but it will always tell a story. Computers will not complain about how you write it (except correct syntax), but human readers will. This talk is about writing clear code and caring for the human beings that will read it. Yourself included.

Upgrade your Documentation to the Next Level
Shivam Singhal
Community, Development Methods

Learn how to write great documentation to nurture community of your open source project

What I learned from monitoring more than 30 Machine Learning Use Cases
Lina Weichbrodt
Best Practice, Backend, DevOps

How to implement #MachineLearning #monitoring for the impatient. Lessons I learned from running more than 30 models in production. And good news, you can use your existing monitoring and dashboard stack like #Prometheus and #Grafana