wednesday Session List
"Easy Python": lies, damned lies, and metaclasses
Grigory Petrov, Maxim Danilov
Best Practice, Coding / Code-Review, Development Methodstop-10 Python complexities and how they are required to fight the "software complexity problem" in big projects
(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.
Advanced Django ORM
Bas Steins
Databases, DjangoLeverage the potential of Django ORM to write complex queries, optimize performance and have fun with constraints
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
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.
Come as you are: Transitioning from Science to Data Science
Dr. Hannah Bohle
Career & FreelancingCome as you are: Transitioning from Science to Data Science. How to find your first job in industry after leaving academia.
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.
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
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!
Financial Portfolio Management with Deep Reinforcement Learning
T-Berger
Neural Networks / Deep Learning, Simulation, Time Seriesintelligent_portfolio_optimization_with_deep_reinforcement_learning
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.
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 Find Your Way Through a Million Lines of Code
Jürgen Gmach
Best PracticeScared of a new project? @jugmac00 will show you "How to Find Your Way Through a Million Lines of Code"
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.
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.
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.
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!
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.
Navigating the limitations of Python’s concurrency model in web services
Tarek Mehrez
APIs, Architecture, Parallel Programming / AsyncEver 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
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.
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 🤩
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.
Refactoring
Dr. Kristian Rother
Best Practice, Coding / Code-Review, Development MethodsRefactor a space travel game by introducing functions, classes and data structures
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.
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 Magic of Python Objects
Coen de Groot
Python fundamentalsDiscover the Magic of Python Objects and the 125+ methods that keep them running
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.
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
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