Grigory Petrov, Maxim DanilovBest Practice, Coding / Code-Review, Development Methods
top-10 Python complexities and how they are required to fight the "software complexity problem" in big projects
Marysia Winkels, James HaywardTime 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.
Jonathan StriebelData 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.
Maarten HuijsmansAPIs, Best Practice
5 the common challenges in building FastAPI apps and how to solve them
Alexander CS HendorfArchitecture, 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.
Reshama ShaikhCommunity, Science, Statistics
In this keynote, I will share highlights, challenges and lessons learned. (https://www.dataumbrella.org/sprints).
Sylvain MariéAlgorithms, Predictive Modelling, Science
`python-m5p` is an implementation of the M5P algorithm compliant with scikit-learn.
Alexandra WörnerCoding / 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.
Ricardo Kawase, Marlene HenseBest 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 mobile.de that support users in making the right decisions.
Bas SteinsDatabases, Django
Leverage the potential of Django ORM to write complex queries, optimize performance and have fun with constraints
Tanmoy BandyopadhyayAlgorithms, Coding / Code-Review, Parallel Programming / Async
Use Python Inter Process Communication and Synchronization techniques effectively
Mike MüllerAlgorithms, Architecture, Python fundamentals
Functions that take functions and return new functions can be fun. Python's everything-is-an-object principle at work.
Jannis GrönbergArchitecture, 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
Melissa Weber MendonçaCommunity, 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.
sonamDiversity & 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 KolawoleComputer 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.
Jonathan Oberländer, Patrick SchemitzArt, Databases
From an empty Python file to a fully-featured ORM in 45 minutes
Sara JakšaAPIs, 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.
Asya FrumkinAlgorithms, 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.
Katharine Jarmul, Matteo Guzzo, Sieer Angar, Marielle Dado, Emily GorcenskiCareer & Freelancing
Are you thinking about a career change? In our career panel we will discuss different aspects with participants from different fields.
Bettina HeinleinComputer Vision
Leveraging problem-solving strategies for challenges in building Computer Vision applications and beyond, illustrated with a recent Computer Vision project.
Dr. Hannah BohleCareer & Freelancing
Come as you are: Transitioning from Science to Data Science. How to find your first job in industry after leaving academia.
Wolf Vollprecht, Jannis Leidel, Jaime Rodríguez-GuerraCommunity, 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?
Martin ChristenGIS / Geo-Analytics
Create 3DMaps anywhere on the planet using Python and OpenData
Stephannie Jimenez GachaAPIs
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.
Richard PelgrimAPIs, Big Data, Cloud
A hands-on introduction to methods for scaling your data science and machine learning with Dask.
Janis MeyerComputer Vision, Natural Language Processing
deepdoctection is a Python package that enables document analysis pipelines to be built using deep learning models.
Sebastiaan ZeeffPython - 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.
Emeli DralBest 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.
GathaData 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
Dr. Setareh SadjadiCareer & Freelancing, Community
Is Data Science really cooling down? Do we need Data Scientists? What for?
Philipp Rudiger, Maxime LiquetData 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.
Vaggelis Papoutsellis, Dr. Jakob Sauer JørgensenAlgorithms, Big Data, Math
Core Imaging Library is an open-source, object-oriented Python library for inverse problems in imaging developed by the UK academic network CCPi.
Maria MestreData Engineering, Data Visualization, Natural Language Processing
Data labelling should not be a waterfall task. Label your data significantly faster with weak supervision (https://github.com/dataqa/dataqa)
Stefan BehnelBig Data, Parallel Programming / Async, Python - PyPy, Cython, Anaconda
Need fast data access in Python? Use native data structures with Cython!
Torsten ZielkeBest 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
T-BergerNeural Networks / Deep Learning, Simulation, Time Series
Antoine ToubhansBest Practice, Computer Vision, Data Engineering, Data Visualization, Development Methods, Reproducibility
Flexible ML Experiment Tracking System for Python Coders with DVC and Streamlit
David MelamedCloud, 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.
Forget ‘web 3.0’, let's talk about ‘web 0.0’. A brief history of the Internet, and the World Wide Web.
Dom WeldonArt, 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.
Somewhat comfortable with using SQL to access data, but curious to know what happens behind the scenes when you send off your query?
Grokking LIME: How can we explain why an image classifier "knows" what’s in a photo without looking inside the model?
Kilian KlugeComputer 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 WilhelmMath, Predictive Modelling, Statistics
Honey, I shrunk the target variable! Common pitfalls when transforming the target variable and how to exploit transformations.
Daniël Willemsen, Welmoet VerbaanBest 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!
Andre FröhlichArchitecture, 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
Gina HäußgeBest 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!
Jürgen GmachBest Practice
Scared of a new project? @jugmac00 will show you "How to Find Your Way Through a Million Lines of Code"
Tilman KrokotschBest Practice, Neural Networks / Deep Learning
Write unit tests and learn to trust your Deep Learning code again.
Riya BansalCommunity, Diversity & Inclusion
Let’s face it. The positive impact of diversity and inclusion is no longer debatable.
Guillaume LemaitrePredictive Modelling, Statistics, Transparency / Interpretability
Inspect and try to interpret your scikit-learn machine-learning models
Guido ImperialeAlgorithms, Architecture, Backend, Cloud, Data Engineering, Distributed Computing, Parallel Programming / Async
The Active Memory Manager is a new experimental feature of Dask which aims to reduce the memory footprint of the cluster, prevent hard to debug out-of-memory issues, and make worker retirement more robust.
Tobias SterbakBest Practice, Predictive Modelling, Reproducibility
Learn the basics of MLops with MLflow to manage the machine learning life-cycle.
Introduction to OPC-UA and industrial IoT: Liberate machines from the proprietary clutches of Big Hardware with the power of opcua-asyncio
Joey FaulknerBackend, Hardware, Networks
Software around industrial hardware is still highly proprietary, which leads to bad UX and inefficient use of hardware. OPC-UA represents an earnest new start at the world of IIoT, and using opcua-asyncio, we can create this revolution in python.
Dr. Juan OrduzAlgorithms, Predictive Modelling, Statistics
In this talk we introduce uplift modelling, a method to estimate conditional average treatment effects (CATE) using machine learning estimators.
Efe ÖgeAPIs, Architecture, Backend, Cloud, DevOps, Django
Managing files won't be easier but more obvious after this talk.
Marianne StecklinaBest 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!
Jeremy TuloupJupyter, 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.
Yunus Emrah BulutComputer 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.
Paolo MelchiorreBest 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
Jan-Benedikt Jagusch, Christian BourjauData 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.
DavidBest 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.
Julia OstheimerBest 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
Illia BabounikauStatistics, 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.
Tarek MehrezAPIs, Architecture, Parallel Programming / Async
Ever wondered when you should favor an async web framework? How do they compare to your good old python services when scaling is in question? Then this is the talk for you
Joris Van den BosscheAPIs, 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
Adrian BoguszewskiJupyter, 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.
Alena GuzharinaData 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.
Sebastian CattesNatural 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.
Valerio MaggioNeural 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
Aleksander MolakGraphs, Neural Networks / Deep Learning
Practical Graph Neural Networks (GNNs) with Spektral & TensorFlow 🤩
Tobias HoinkaPredictive 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.
Travis HathawayData 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.
Florian BruhinBest 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.
Laysa UchoaBest 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! 🔥
Christian HeimesPython - CPython new features
Compile CPython to Web Assembly, and run it in web browsers or Node.js.
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.
Philipp StephanBest 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 RotherBest Practice, Coding / Code-Review, Development Methods
Refactor a space travel game by introducing functions, classes and data structures
Prabhant SinghBest Practice, Community, Science
Learn how to create reproducible workflows, benchmarks and studies with openml-python
Helena SchmidtBest 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?
Daniel RinglerData Visualization, Jupyter, Python fundamentals
Sankey Plots in Python? Get an introduction on how and when to use them.
Alejandro SaucedoBest 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.
Gajendra DeshpandeBest 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.
Jean-Luc StevensBig Data, Data Visualization, Jupyter
Building simple custom interactive web dashboards that display millions or billions of samples while giving access to each individual sample.
sktime - python toolbox for time series: advanced forecasting - probabilistic, global and hierarchical
Franz KiralyAlgorithms, 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.
Jordi SmitAPIs, 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.
Adam SerafiniPackaging, Performance
Let's speed up Python, with Zig! A tour through Python's C API and packaging challenges...
Dr. Thomas WollmannDistributed Computing, Neural Networks / Deep Learning, Parallel Programming / Async
Learn why we built and open sourced a data infrastructure library for deep learning.
Mark SmithBest 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.
Coen de GrootPython fundamentals
Discover the Magic of Python Objects and the 125+ methods that keep them running
Stefanie StoppelEthics (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.
Shir Meir LadorBest 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.
Tobias HeintzData 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.
Miroslav ŠedivýCoding / 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?
Sebastian Wanner, Christopher LennanNatural 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.
Cheuk Ting HoCommunity, 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?
Dario CannoneBest 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.
Andreu MoraAlgorithms, 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
Shivam SinghalCommunity, Development Methods
Learn how to write great documentation to nurture community of your open source project
Jacopo FarinaData Engineering, Databases
Lessons learned in 4 years using Postgres in a machine learning project
Jessica Greene (she/her), Vanessa AguilarData 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!
Jannis LübbeAPIs, Data Visualization, Use Case
Streaming sensor data to multiple end devices using FastAPI and websockets.
Theodore MeynardBest 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.
Lina WeichbrodtBest 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
Larissa HaasData Visualization, Ethics (Privacy, Fairness,… ), Transparency / Interpretability
XAI meets NLP - approaches, workarounds and lessons learned while making an NLP project explainable
Gönül AycıEthics (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 ⚡️
Paula Gonzalez AvalosData 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.