"Easy Python": lies, damned lies, and metaclasses
Grigory Petrov, Maxim Danilov
Best Practice, Coding / Code-Review, Development Methods

top-10 Python complexities and how they are required to fight the "software complexity problem" in big projects

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.

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.

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.

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

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!

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"

How to Trust Your Deep Learning Code
Tilman Krokotsch
Best Practice, Neural Networks / Deep Learning

Write unit tests and learn to trust your Deep Learning code again.

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.

jsonargparse - Say goodbye to configuration hassles
Marianne Stecklina
Best Practice

A proper CLI would be nice, but you're way too lazy to write it? Join this talk to learn about the open-source library jsonargparse!

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 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

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.

Python 3.10: Welcome to pattern matching!
Laysa Uchoa
Best Practice, Coding / Code-Review, Python fundamentals

Python 3.10: let us learn about Pattern Matching. In this presentation, you will be surprised how simple, yet powerful, Pattern Matching really is. This talk and you, it is a match! 🔥

Quitting pip: How we use git submodules to manage internal dependencies that require fast iteration
Philipp Stephan
Best Practice, Development Methods, DevOps, Packaging

After a review of the current state of Python dependency management, we’d like to present a versatile method of using git submodules to handle internal dependencies in a dockerized microservice architecture, where common libraries have to be iterated quickly.

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

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?

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.

Securing Django Applications
Gajendra Deshpande
Best Practice, Django, Security

In this talk, we will focus on two aspects. First, performing penetration testing on Django web applications to identify vulnerabilities and scanning for OWASP Top 10 risks. Second, strategies and configuration settings for making the source code and Django applications secure.

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 secret sauce of data science management
Shir Meir Lador
Best Practice, Big Data, Career & Freelancing, Corporate

In this talk, we will discuss lessons learned on how to build a DS team that prospers while addressing the unique challenges of leading a DS team.

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

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.

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