Domain Expertise some Session List
5 Steps to Speed Up Your Data-Analysis on a Single Core
Jonathan Striebel
Data Engineering, PerformanceYour 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 Practice5 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-ReviewCode 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 / AsyncUse Python Inter Process Communication and Synchronization techniques effectively
Battle of Pipelines - who will win python orchestration in 2022?
Jannis Grönberg
Architecture, Data Engineering, DevOpsYou 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
sonam
Diversity & Inclusion, Ethics (Privacy, Fairness,… ), Natural Language ProcessingStudy 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 LearningBuilding 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 LearningI 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
APIsIntroduction 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 Processingdeepdoctection 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 fundamentalsDo 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, StatisticsWhen 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?
Gatha
Data Engineering, Ethics (Privacy, Fairness,… ), GovernanceSynthetic 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, ScienceDo 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-ReviewLearn 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
T-Berger
Neural Networks / Deep Learning, Simulation, Time Seriesintelligent_portfolio_optimization_with_deep_reinforcement_learning
Flexible ML Experiment Tracking System for Python Coders with DVC and Streamlit
Antoine Toubhans
Best Practice, Computer Vision, Data Engineering, Data Visualization, Development Methods, ReproducibilityFlexible 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, SecurityNo 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
DatabasesSomewhat 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, StatisticsHoney, 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 CaseThis 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 LearningWrite 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 / InterpretabilityInspect and try to interpret your scikit-learn machine-learning models
Introduction to MLOps with MLflow
Tobias Sterbak
Best Practice, Predictive Modelling, ReproducibilityLearn 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, DjangoManaging 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, PackagingIn 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
David
Best Practice, Development Methods, ReproducibilityIn 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 CaseYou 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 SeriesEstablished 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 StructuresAs 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, PerformanceLearn 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, ReproducibilityOvercoming 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, StatisticsCan 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, SecurityHave 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 LearningPractical Graph Neural Networks (GNNs) with Spektral & TensorFlow 🤩
Processing Open Street Map Data with Python and PostgreSQL
Travis Hathaway
Data Engineering, Databases, GIS / Geo-AnalyticsOpen 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 fundamentalsPython 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 featuresCompile 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, PackagingAfter 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, ScienceLearn how to create reproducible workflows, benchmarks and studies with openml-python
Rewriting your R analysis code in Python
Helena Schmidt
Best Practice, Development Methods, RR 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 fundamentalsSankey Plots in Python? Get an introduction on how and when to use them.
Securing Django Applications
Gajendra Deshpande
Best Practice, Django, SecurityIn 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 SeriesThe 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 CaseMost 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, PerformanceLet'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 / AsyncLearn 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, CorporateIn 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, DevOpsHow 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 fundamentalsShould 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 CaseTransformer 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 / InterpretabilityTrojan 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 ModellingTune 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 MethodsLearn 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, DatabasesLessons 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, PerformanceWe 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 CaseStreaming 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 EngineeringThis 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, DevOpsHow 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 / InterpretabilityXAI meets NLP - approaches, workarounds and lessons learned while making an NLP project explainable
You shall not share!
Gönül Aycı
Ethics (Privacy, Fairness,… ), Natural Language ProcessingAre 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 ⚡️
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