5 Steps to Speed Up Your Data-Analysis on a Single Core
Jonathan Striebel
Data Engineering, Performance

Your data analysis pipeline works. Nice. Could it be faster? Probably. Do you need to parallelize? Not yet. Discover optimization steps that boost the performance of your data analysis pipeline on a single core, reducing time & costs.

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

`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.

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

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

Building a Sign-to-Speech prototype with TensorFlow, Pytorch and DeepStack: How it happened & What I learned
Steven Kolawole
Computer Vision, Neural Networks / Deep Learning

Building an E2E working prototype that detects sign language meanings in images/videos and generate equivalent voice of words communicated by the sign language, in real-time, won't be completed in a day's work. Here I'd explain how it happened and what I learned in the process.

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.

Data Apis: Standardization of N-dimensional arrays and dataframes
Stephannie Jimenez Gacha

Introduction to the consortium of Data APIs, where we will be presenting our motivation, objectives and progress of the standardization process after one year of activity.

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.

Demystifying Python's Internals: Diving into CPython by implementing a pipe operator
Sebastiaan Zeeff
Python - CPython new features, Python fundamentals

Do you want to dive into the CPython Source Code but feel a bit overwhelmed? Watch Sebastiaan Zeeff demystify CPython's Internals by taking you through the implementation of a new operator.

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 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

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

Financial Portfolio Management with Deep Reinforcement Learning
Neural Networks / Deep Learning, Simulation, Time Series


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 Mono vs. Multi-Repo - Building Centralized Git Workflows with Python
David Melamed
Cloud, Coding / Code-Review, DevOps, Security

No need to reinvent the CI/CD wheel for every service - learn how to build centralized git workflows for all your repos in Python.

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?

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 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 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.

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.

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.

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.

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

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.

On Blocks, Copies and Views: updating pandas' internals
Joris Van den Bossche
APIs, Data Structures

As a pandas user, did you ever run into the SettingWithCopyWarning? Quite likely, and this is one of the more confusing aspects of pandas. But it doesn’t have to be this way! Check my proposal to simplify this aspect of pandas

Optimize your network inference time with OpenVINO
Adrian Boguszewski
Jupyter, Neural Networks / Deep Learning, Performance

Learn how to automatically convert the model using Model Optimizer and how to run the inference with OpenVINO Runtime to infer your model with low latency on the CPU and iGPU you already have. The magic with only a few lines of code.

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.

Performing Content: Can NLP and Deep Learning algorithms predict reader preferences?
Sebastian Cattes
Natural Language Processing, Neural Networks / Deep Learning, Statistics

Can AI understand what drives user engagement? Join our talk "Performing Content: Can NLP and Deep Learning algorithms predict reader preferences?" to find out what NLP can bring to the editorial table.

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 🤩

Processing Open Street Map Data with Python and PostgreSQL
Travis Hathaway
Data Engineering, Databases, GIS / Geo-Analytics

Open Street Map is a large, community supported data set covering the entire world. Learn how to process this data with Python and PostgreSQL as I walk you through creating projects of your own. Along the way, we learn how OSM data is structured, and how you can use it yourself.

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! 🔥

Python 3.11 in the Web Browser - A Journey
Christian Heimes
Python - CPython new features

Compile CPython to Web Assembly, and run it in web browsers or Node.js.

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.

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?

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.

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.

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.

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...

Squirrel - Efficient Data Loading for Large-Scale Deep Learning
Dr. Thomas Wollmann
Distributed Computing, Neural Networks / Deep Learning, Parallel Programming / Async

Learn why we built and open sourced a data infrastructure library for deep learning.

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.

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.

There Are Python 2 Relics in Your Code
Miroslav Šedivý
Coding / Code-Review, Python - CPython new features, Python fundamentals

Should we return to Python 2 or should we get rid of all Python 2 relics from our code?

Transformer based clustering: Identifying product clusters for E-commerce
Sebastian Wanner, Christopher Lennan
Natural Language Processing, Neural Networks / Deep Learning, Use Case

Transformer based clustering with Sentence-Transformers and Facebook Faiss for an E-commerce use case where we clustered offers to automatically generate new products.

Trojan Source Malware - Can we trust open-source anymore?
Cheuk Ting Ho
Community, Governance, Python fundamentals, Security, Transparency / Interpretability

Trojan Source Malware has been tested on Python and it works. Shall the Python and open-source communities be concerned?

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

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

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.

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

You shall not share!
Gönül Aycı
Ethics (Privacy, Fairness,… ), Natural Language Processing

Are you ready to have an agent to help to preserve your privacy in online social networks? "You shall not share!" will be presented by @gonul_ayci ⚡️