5 Things we've learned building large APIs with FastAPI
Maarten Huijsmans
APIs, Best Practice

5 the common challenges in building FastAPI apps and how to solve them

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. (

`python-m5p` - M5 Prime regression trees in python, compliant with scikit-learn
Sylvain Marié
Algorithms, Predictive Modelling, Science

`python-m5p` is an implementation of the M5P algorithm compliant with scikit-learn.

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.

Aspect-oriented Programming - Diving deep into Decorators
Mike Müller
Algorithms, Architecture, Python fundamentals

Functions that take functions and return new functions can be fun. Python's everything-is-an-object principle at work.

Battle of Pipelines - who will win python orchestration in 2022?
Jannis Grönberg
Architecture, Data Engineering, DevOps

You struggle choosing the right #orchestration tool in #Python ? Join this #PyCon talk about when it's best to use #Kubeflow, #Airflow or #Prefect and learn how to automate your #data #pipelines and #ML workflows. #DataScience #dataengineering #DevOps #MLOps

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

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

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.

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.

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?

Data Science at Scale with Dask
Richard Pelgrim
APIs, Big Data, Cloud

A hands-on introduction to methods for scaling your data science and machine learning with Dask.

deepdoctection - An open source package for document intelligence
Janis Meyer
Computer Vision, Natural Language Processing

deepdoctection is a Python package that enables document analysis pipelines to be built using deep learning models.

Detecting drift: how to evaluate and explore data drift in machine learning systems
Emeli Dral
Best Practice, Data Visualization, Statistics

When ML model is in production, you might encounter data and prediction drift. How exactly to detect and evaluate it? I'll share in this talk.

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?

Easily build interactive plots and apps with hvPlot
Philipp Rudiger, Maxime Liquet
Data Visualization, Jupyter, Science

Do you use the .plot() API of pandas or xarray? Do you ever wish it was easier to try out different combinations of the parameters in your data-processing pipeline? Follow this tutorial to learn how to easily build interactive plots and apps with hvPlot.

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 (

Faster Workflow with Testdriven Development
Torsten Zielke
Best Practice, Backend, Coding / Code-Review

Learn how to use testdriven development to boost your productivity and let the community do the annoying frequent checkups if the application still works

Flexible ML Experiment Tracking System for Python Coders with DVC and Streamlit
Antoine Toubhans
Best Practice, Computer Vision, Data Engineering, Data Visualization, Development Methods, Reproducibility

Flexible ML Experiment Tracking System for Python Coders with DVC and Streamlit

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.

Grokking LIME: How can we explain why an image classifier "knows" what’s in a photo without looking inside the model?
Kilian Kluge
Computer Vision, Neural Networks / Deep Learning, Transparency / Interpretability

How can LIME explain machine-learning models without peeking inside? Let's find out!

Honey, I shrunk the target variable! Common pitfalls when transforming the target variable and how to exploit transformations.
Florian Wilhelm
Math, Predictive Modelling, Statistics

Honey, I shrunk the target variable! Common pitfalls when transforming the target variable and how to exploit transformations.

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 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!

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.

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.

Machine Learning Testing Ecosystem of Python
Yunus Emrah Bulut
Computer Vision, Ethics (Privacy, Fairness,… ), Governance, Natural Language Processing, Neural Networks / Deep Learning, Security

Machine learning testing becomes an indispensable part of the MLOps and Python offers great ecosystem for this purpose.

Make the most of Django
Paolo Melchiorre
Best Practice, Community, Django

🐍 "Make the most of Django" 👉 Taking full advantage of #OpenSource software means getting involved in its #community and #contributing to its development. We'll see how this is profoundly true in the #Django case as well. #pyconde #talk #python

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

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.

Overcoming 5 Hurdles to Using Jupyter Notebooks for Data Science, by the JetBrains Datalore Team
Alena Guzharina
Data Visualization, Jupyter, Reproducibility

Overcoming 5 Hurdles to Using Jupyter Notebooks for Data Science, by the JetBrains @Datalore Team Join our talk to discuss setting up environments, working with data, writing code without IDE support, and sharing results, as well as collaboration and reproducibility.

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.

Reproducible machine learning and science with python
Prabhant Singh
Best Practice, Community, Science

Learn how to create reproducible workflows, benchmarks and studies with openml-python

Sankey Plots with Python
Daniel Ringler
Data Visualization, Jupyter, Python fundamentals

Sankey Plots in Python? Get an introduction on how and when to use them.

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.

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.

Speeding up Python with Zig
Adam Serafini
Packaging, Performance

Let's speed up Python, with Zig! A tour through Python's C API and packaging challenges...

Stupid Things I've Done With Python
Mark Smith
Best Practice, Coding / Code-Review, Python fundamentals

On every computer I've had for the past 20 years, I've created a folder called "stupid python tricks". It's where I put code that should never see the light of day. Code I'm going to teach you.

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.

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.

Unsupervised shallow learning for fraud detection on marketplaces
Andreu Mora
Algorithms, Best Practice, Predictive Modelling

Tune in to learn how @adyen uses ML and open source over python to combat fraud and wrongdoings over large marketplaces such as @gofundme or @eBay

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

We know what your app did last summer. Do you? Observing Python applications using Prometheus.
Jessica Greene (she/her), Vanessa Aguilar
Data Visualization, DevOps, Performance

We know what your app did last summer. Do you? Join us for this practical & theoretical session if you’re looking to grasp the key concepts of observability, useful metrics, and ensuring operational excellence for your Python applications using Prometheus!

Web based live visualisation of sensor data
Jannis Lübbe
APIs, Data Visualization, Use Case

Streaming sensor data to multiple end devices using FastAPI and websockets.

What are data unit tests and why we need them
Theodore Meynard
Best Practice, Data Engineering

This talk will introduce the concept of data unit tests and why they are important in the workflow of data scientists when building data products.

Your data, your insights: creating personal data projects to (re-)own the data you share
Paula Gonzalez Avalos
Data Visualization, Predictive Modelling

Your data, your insights: 3 examples to illustrate how we can apply common data science libraries together with data shared via mobile apps or collected manually to build little data visualization projects that provide unique, contextual and intmiate insights.