(Serious) Time for Time Series
Marysia Winkels, James Hayward
Time Series

From inventory to website visitors, resource planning to financial data, time-series data is all around us. Knowing what comes next is key to success in this dynamically changing world. So join us and learn about time series analysis and seasonality modelling.

An Introduction to Inter Process Communication and Synchronization using Python
Tanmoy Bandyopadhyay
Algorithms, Coding / Code-Review, Parallel Programming / Async

Use Python Inter Process Communication and Synchronization techniques effectively

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.

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.

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.

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

Inpsect and try to interpret your scikit-learn machine-learning models
Guillaume Lemaitre
Predictive Modelling, Statistics, Transparency / Interpretability

Inspect and try to interpret your scikit-learn machine-learning models

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 MLOps uncool again
Best Practice, Development Methods, Reproducibility

In this workshop, we will learn what it means and how to build an "MLOps workflow" by extending the power of Git and GitHub with open-source tools.

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

PPML: Machine Learning on Data you cannot see
Valerio Maggio
Neural Networks / Deep Learning, Security

Have you ever wondered how to train your @PyTorch model on private data you cannot see? If you want to know how, this is the workshop for you! #PPML cc/ @openminedorg

Practical graph neural networks in Python with TensorFlow and Spektral
Aleksander Molak
Graphs, Neural Networks / Deep Learning

Practical Graph Neural Networks (GNNs) with Spektral & TensorFlow 🤩

pytest - simple, rapid and fun testing with Python (3 hours)
Florian Bruhin
Best Practice, Development Methods

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.

Dr. Kristian Rother
Best Practice, Coding / Code-Review, Development Methods

Refactor a space travel game by introducing functions, classes and data structures

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

sktime - python toolbox for time series: advanced forecasting - probabilistic, global and hierarchical
Franz Kiraly
Algorithms, Predictive Modelling, Time Series

The forecasting module of sktime provides a unified, sklearn-compatible, and composable interface. This tutorial covers advanced topics in forecasting using sktime: probabilistic forecasting, and forecasting with panel data, including global/hierarchical forecasting.

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!