Python Skill Level some Session List
(Serious) Time for Time Series
Marysia Winkels, James Hayward
Time SeriesFrom 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.
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.
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
Aspect-oriented Programming - Diving deep into Decorators
Mike Müller
Algorithms, Architecture, Python fundamentalsFunctions that take functions and return new functions can be fun. Python's everything-is-an-object principle at work.
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
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.
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.
Data Science at Scale with Dask
Richard Pelgrim
APIs, Big Data, CloudA hands-on introduction to methods for scaling your data science and machine learning with Dask.
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.
Easy and flexible imaging with the Core Imaging Library
Vaggelis Papoutsellis, Dr. Jakob Sauer Jørgensen
Algorithms, Big Data, MathCore Imaging Library is an open-source, object-oriented Python library for inverse problems in imaging developed by the UK academic network CCPi.
Fast native data structures: C/C++ from Python
Stefan Behnel
Big Data, Parallel Programming / Async, Python - PyPy, Cython, AnacondaNeed fast data access in Python? Use native data structures with Cython!
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.
Grokking LIME: How can we explain why an image classifier "knows" what’s in a photo without looking inside the model?
Kilian Kluge
Computer Vision, Neural Networks / Deep Learning, Transparency / InterpretabilityHow 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 Wilhelm
Math, Predictive Modelling, StatisticsHoney, I shrunk the target variable! Common pitfalls when transforming the target variable and how to exploit transformations.
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
Introducing the Dask Active Memory Manager
Guido Imperiale
Algorithms, Architecture, Backend, Cloud, Data Engineering, Distributed Computing, Parallel Programming / AsyncThe 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.
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.
Introduction to OPC-UA and industrial IoT: Liberate machines from the proprietary clutches of Big Hardware with the power of opcua-asyncio
Joey Faulkner
Backend, Hardware, NetworksSoftware 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.
jsonargparse - Say goodbye to configuration hassles
Marianne Stecklina
Best PracticeA 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!
Machine Learning Testing Ecosystem of Python
Yunus Emrah Bulut
Computer Vision, Ethics (Privacy, Fairness,… ), Governance, Natural Language Processing, Neural Networks / Deep Learning, SecurityMachine learning testing becomes an indispensable part of the MLOps and Python offers great ecosystem for this purpose.
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
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.
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.
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.
pytest - simple, rapid and fun testing with Python (3 hours)
Florian Bruhin
Best Practice, Development MethodsThe #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 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! 🔥
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.
Refactoring
Dr. Kristian Rother
Best Practice, Coding / Code-Review, Development MethodsRefactor a space travel game by introducing functions, classes and data structures
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.
Seeing the needle AND the haystack: single-datapoint selection for billion-point datasets
Jean-Luc Stevens
Big Data, Data Visualization, JupyterBuilding 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 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...
Stupid Things I've Done With Python
Mark Smith
Best Practice, Coding / Code-Review, Python fundamentalsOn 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 Magic of Python Objects
Coen de Groot
Python fundamentalsDiscover the Magic of Python Objects and the 125+ methods that keep them running
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.
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?
Unclear Code Hurts
Dario Cannone
Best Practice, Coding / Code-ReviewCode 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 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
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.
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 ⚡️
Your data, your insights: creating personal data projects to (re-)own the data you share
Paula Gonzalez Avalos
Data Visualization, Predictive ModellingYour 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.
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