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.

5 Years, 10 Sprints, A scikit-learn Open Source Journey
Reshama Shaikh
Community, Science, Statistics

In this keynote, I will share highlights, challenges and lessons learned. (

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.

Beyond the basics: Contributor experience, diversity and culture in open source projects
Melissa Weber Mendonça
Community, Diversity & Inclusion, Governance

In this talk, we'll explore actions and assumptions about DEI and how they relate to volunteer work and open-source communities, how we can go beyond the basics when engaging new contributors, and improving a project's culture around inclusiveness and different axes of diversity.

But this is an OAuth, is it not?
Sara Jakša
APIs, Backend

OAuth simplified and secured third-party integrations for the end user. But for the developer of the integration, it can still present some friction. This talk talks about examples of real-life problems that were encountered by implementing multiple OAuth integrations.

Can you Read This? (Or: how I Improved Text Readability on the Web for the Visually Impaired)
Asya Frumkin
Algorithms, Computer Vision, Neural Networks / Deep Learning

I will explain my approach of detecting texts on top of an image background that are unreadable to people with visual impairment. I will explain the challenges I. encountered when using different OCR architectures for this task and talk about the solution I came up with.

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.

Challenge Accepted - How to Escape the Quicksand While Engineering a Computer Vision Application
Bettina Heinlein
Computer Vision

Leveraging problem-solving strategies for challenges in building Computer Vision applications and beyond, illustrated with a recent Computer Vision project.

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?

Creating 3D Maps using Python
Martin Christen
GIS / Geo-Analytics

Create 3DMaps anywhere on the planet using Python and OpenData

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?

Efficient data labelling with weak supervision
Maria Mestre
Data Engineering, Data Visualization, Natural Language Processing

Data labelling should not be a waterfall task. Label your data significantly faster with weak supervision (

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.

Fundamentals of relational databases
Katharina Rasch

Somewhat comfortable with using SQL to access data, but curious to know what happens behind the scenes when you send off your query?

How a simple streamlit dashboard will help to put your machine learning model in production
Daniël Willemsen, Welmoet Verbaan
Best Practice, Data Visualization, Predictive Modelling

Have you struggled getting your valuable machine learning model into the hands of users? A simple streamlit monitoring dashboard can help!

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!

How to Find Your Way Through a Million Lines of Code
Jürgen Gmach
Best Practice

Scared of a new project? @jugmac00 will show you "How to Find Your Way Through a Million Lines of Code"

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 Uplift Modeling
Dr. Juan Orduz
Algorithms, Predictive Modelling, Statistics

In this talk we introduce uplift modelling, a method to estimate conditional average treatment effects (CATE) using machine learning estimators.

It is all about files and HTTP
Efe Öge
APIs, Architecture, Backend, Cloud, DevOps, Django

Managing files won't be easier but more obvious after this talk.

JupyterLite: Jupyter ❤️ WebAssembly ❤️ Python
Jeremy Tuloup
Jupyter, Reproducibility, Use Case

JupyterLite is a Jupyter distribution that runs entirely in the web browser, backed by in-browser language kernels such as the WebAssembly powered Pyodide kernel. JupyterLite enables data science and interactive computing with the PyData scientific stack, directly in the browser.

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

My forecast is better than yours! What does that even mean?
Illia Babounikau
Statistics, Time Series

Established forecast evaluation procedures often turn out to be inappropriate and biased for modern time series forecasting. I will present the number of forecast evaluations issues and resolutions based on the real use cases of demand forecasting developed within BlueYonder.

Predictive Maintenance and Anomaly Detection for Wind Energy
Tobias Hoinka
Predictive Modelling, Statistics, Time Series

This talk will describe predictive modeling applications in wind turbine maintenance, the challenges of anomaly detection and ways to move to more automatic diagnoses by modeling past documented defects.

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.

Secure ML: Automated Security Best Practices in Machine Learning
Alejandro Saucedo
Best Practice, Data Engineering, Security

As data science capabilities scale, the core concept of security becomes growingly critical - in this talk we provide an overview of challenges, solutions and best practices to introduce security into the ML lifecycle.

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.

The state of DevOps for Python projects
Tobias Heintz
Data Engineering, Development Methods, DevOps

How alcemy uses DevOps techniques to streamline and accelerate our daily development. Let's look at a number of real-world examples and best practices taken straight from the pipelines we use to release code several times a day.

Using a database in a data science project - Lessons learned in production
Jacopo Farina
Data Engineering, Databases

Lessons learned in 4 years using Postgres in a machine learning 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

XAI meets Natural Language Processing
Larissa Haas
Data Visualization, Ethics (Privacy, Fairness,… ), Transparency / Interpretability

XAI meets NLP - approaches, workarounds and lessons learned while making an NLP project explainable