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
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.
Tanmoy BandyopadhyayAlgorithms, Coding / Code-Review, Parallel Programming / Async
Use Python Inter Process Communication and Synchronization techniques effectively
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
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.
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.
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.
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
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.
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.
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 WilhelmMath, Predictive Modelling, Statistics
Honey, I shrunk the target variable! Common pitfalls when transforming the target variable and how to exploit transformations.
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
Tilman KrokotschBest Practice, Neural Networks / Deep Learning
Write unit tests and learn to trust your Deep Learning code again.
Guillaume LemaitrePredictive Modelling, Statistics, Transparency / Interpretability
Inspect and try to interpret your scikit-learn machine-learning models
Tobias SterbakBest Practice, Predictive Modelling, Reproducibility
Learn the basics of MLops with MLflow to manage the machine learning life-cycle.
Efe ÖgeAPIs, Architecture, Backend, Cloud, DevOps, Django
Managing files won't be easier but more obvious after this talk.
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.
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 🤩
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.
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.
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.
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.
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.
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.
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?
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 ⚡️