Python Skill Level Intermediate Session List
5 Things about fastAPI I wish we had known beforehand
Alexander CS Hendorf
5 Things about fastAPI I wish we had known beforehand - An opinionated talk about fastAPI in practice.
A concrete guide to time-series databases with Python
Heiner Tholen, Ellen König
A concrete guide to time-series databases with Python - how to choose the right time-series database for your application.
Accelerating Public Consultations with Large Language Models: A Case Study from the UK Planning Inspectorate
Michele Dallachiesa, Andreas Leed
New study shows Large Language Models can accelerate public consultations by streamlining the analysis process of representations for Local Plans. Results show the potential for 30% faster analysis time and up to 90% classification accuracy #AI #NLP #DataScience #pyconde @PINSgov
Accelerating Python Code
Jens Nie
Struggling to get your Python simulation prototype to production because you think it's too slow? Let's speed it up using #PyPy, #numpy, #numba and friends.
Actionable Machine Learning in the Browser with PyScript
Valerio Maggio
Interactive ML apps in the browser with zero installation and no server needed? Come to my talk to know how..
Advanced Visual Search Engine with Self-Supervised Learning (SSL) Representations and Milvus
Antoine Toubhans, Noé Achache
Building a Visual Search Engine with Milvus and comparing supervised and self-supervised approaches for images representations
An unbiased evaluation of environment management and packaging tools
Anna-Lena Popkes
Python packaging is quickly evolving and new tools pop up on a regular basis. Lots of talks and posts on packaging exist but none of them give a structured, unbiased overview of the available tools. Let's change this!
Apache Arrow: connecting and accelerating dataframe libraries across the PyData ecosystem
Joris Van den Bossche
Connecting and accelerating dataframe libraries across the PyData ecosystem with Apache Arrow. Learn about the recent developments in Arrow and its adoption, and how it can improve your day-to-day data analytics workflows.
Aspect-oriented Programming - Diving deep into Decorators
Mike Müller
Effectively programming cross-cutting task with decorators - Code re-use via the @ symbol
AutoGluon: AutoML for Tabular, Multimodal and Time Series Data
Caner Turkmen, Oleksandr Shchur
Learn about #AutoML and @AutoGluon, which can handle a range of tasks from regression to image classification and time series forecasting with state-of-the-art performance. #AutoML #datascience
Bayesian Marketing Science: Solving Marketing's 3 Biggest Problems
Dr. Thomas Wiecki
A Bayesian modeling toolkit to solve today's biggest marketing challenges.
BHAD: Explainable unsupervised anomaly detection using Bayesian histograms
Alexander Vosseler
We present a Bayesian histogram anomaly detector (BHAD). BHAD scales linearly with the size of the data and allows a direct explanation of individual anomaly scores due to its simple linear form
BLE and Python: How to build a simple BLE project on Linux with Python
Bruno Vollmer
Learn what BLE is and how to use it with Python. @bvollmer5 shows in this talk how you can easily build a Linux-based BLE server for your next project.
Building a Personal Assistant With GPT and Haystack: How to Feed Facts to Large Language Models and Reduce Hallucination.
Mathis Lucka
Building a Personal Assistant With GPT and Haystack: How to Feed Facts to Large Language Models and Reduce Hallucination.
Building Hexagonal Python Services
Shahriyar Rzayev
Building Hexagonal Python Services from scratch using Repository, Unit of Work and Use Cases patterns
Cloud Infrastructure From Python Code: How Far Could We Go?
Etzik Bega, Asher Sterkin
Why Infrastructure as Code is not enough and what needs to be done to make Python trully cloud-native programming language?
Code Cleanup: A Data Scientist's Guide to Sparkling Code
Corrie Bartelheimer
Does your production code look like it’s been copied from Untitled12.ipynb? Are your engineers complaining about the code but nobody got time to clean things up? Check out this talk to learn some of the basics of clean coding and how to implement them in a data science team.
Driving down the Memray lane - Profiling your data science work
Cheuk Ting Ho
You should profile your data science work. In this talk, we will introduce Mamray its new Jupyter plugin.
Dynamic pricing at Flix
Amit Verma
How Flixbus designed dynamic pricing strategy according to market demands
evosax: JAX-Based Evolution Strategies
Robert Lange
Tired of having to handle asynchronous processes for neuroevolution? Do you want to leverage high-throughput accelerators for evolution strategies (ES)? evosax allows you to leverage JAX, XLA compilation & auto-vectorization/parallelization to scale ES to accelerators.
Exploring the Power of Cyclic Boosting: A Pure-Python, Explainable, and Efficient ML Method
Felix Wick
We just open-sourced Cyclic Boosting, a pure-Python ML algorithm that's explainable, accurate, robust, easy to use, and fast! Learn more in our presentation #CyclicBoosting #MachineLearning #OpenSource
FastAPI and Celery: Building Reliable Web Applications with TDD
Avanindra Kumar Pandeya
Build reliable and maintainable APIs with FastAPI and Celery using test-driven development (TDD)! Learn how to set up a testing environment, write unit and integration tests, and use mocks and fixtures to isolate and control the tests.
From notebook to pipeline in no time with LineaPy
Thomas Fraunholz
The nightmare before data science production: You found a working prototype for your problem using a Jupyter notebook and now it's time to build a production grade solution from that notebook. The good news is, there's finally a cure: The open-source python package LineaPy!
Geospatial Data Processing with Python: A Comprehensive Tutorial
Martin Christen
Learn how to use Python to process geospatial data in this comprehensive tutorial! You'll gain hands-on experience with many Geo modules, learning how to read and write spatial data, perform coordinate system transformations, create interactive maps, and more.
Getting started with JAX
Simon Pressler
Getting Started with JAX! Hands-on tips to overcome your first hurdles.
Giving and Receiving Great Feedback through PRs
David Andersson
Do you struggle with PRs? Have you ever had to change code even though you disagreed with the change? Have you ever given feedback only to get into a comment war? We'll discuss how to give and receive feedback optimally without the communication problems
Grokking Anchors: Uncovering What a Machine-Learning Model Relies On
KIlian Kluge
What makes or breaks a machine-learning model's decision? Let's use anchor explanations to find out!
Haystack for climate Q/A
Vibha Vikram Rao
Haystack for climate Q/A - How to build POCs quickly and take it to production
Honey, I broke the PyTorch model >.< - Debugging custom PyTorch models in a structured manner
Clara Hoffmann
Honey, I broke the Pytorch model >.< No problem! In this talk, we'll build a toolbox to debug our models and prevent this from happening again -all by leveraging DL logic, synthetic data and pytest. Let's make our models unbreakable <3
How to build observability into a ML Platform
Alicia Bargar
Check out Shopify's talk on how to build observability into a #machinelearing platform. They'll share key learnings on how to track model performance, catch unexpected behaviour & how observability could work with large language models and Chat AIs
How to connect your application to the world (and avoid sleepless nights)
Luis Fernando Alvarez
Come and explore some of the common techniques to help you build reliable distributed systems in Python
How to teach NLP to a newbie & get them started on their first project
Lisa Andreevna Chalaguine
Learn how to teach people to analyse textual data with the help of Python
Incorporating GPT-3 into practical NLP workflows
Ines Montani
Large language models like @OpenAI GPT-3 can complement existing machine learning workflows really well. You can get initial annotations from GPT-3, quickly fix them with an annotation tool like https://prodi.gy , and train a cheaper and better model.
Introduction to Async programming
Dishant Sethi
Asynchronous programming has been gaining a lot of attention in the past few years, and for good reason. This session is going to be an intro to async programming in python.
Large Scale Feature Engineering and Datascience with Python & Snowflake
Michael Gorkow
Learn how Snowpark for Python enables large scale feature engineering and data science!
Let's contribute to pandas (3 hours) #1
Noa Tamir, Patrick Hoefler
Join our beginner friendly, mentored contributing to @pandas_dev workshop at PyData Berlin! 🥳 #opensource #pandas
Let's contribute to pandas (3 hours) #2
Noa Tamir, Patrick Hoefler
Join our beginner friendly, mentored contributing to @pandas_dev workshop at PyData Berlin! 🥳 #opensource #pandas
Machine Learning Lifecycle for NLP Classification in E-Commerce
Gunar Maiwald, Tobias Senst
idealo.de presents its MLOps solution and ML lifecycle for product classification
Maximizing Efficiency and Scalability in Open-Source MLOps: A Step-by-Step Approach
Paul Elvers
Novel approach to #MLOps combines open-source tech with cloud computing to build scalable, maintainable ML system accessible to ML Engineers & Data Scientists.
Methods for Text Style Transfer: Text Detoxification Case
Daryna Dementieva
How to detoxify texts? How to collect parallel corpus for text style transfer task? How to transfer the knowledge of a style between languages? We answer these questions in this talk.
MLOps in practice: our journey from batch to real-time inference
Theodore Meynard
I will present the challenges we encountered while migrating an ML model from batch to real-time predictions and how we handled them.
Modern typed python: dive into a mature ecosystem from web dev to machine learning
samsja
Typing is at the center of „modern Python“, and tools (mypy, beartype) and libraries (FastAPI, SQLModel, Pydantic, DocArray) based on it are slowly eating the Python world. This talks explores the benefits of Python type hints, and shows how they are infiltrating the next big do
Monorepos with Python
AbdealiLoKo
Monorepos have been successful in other communities - how does it work in Python ?
Most of you don't need Spark. Large-scale data management on a budget with Python
Guillem Borrell
Most of you don't need Spark. Large-scale data management on a budget with Python
Neo4j graph databases for climate policy
Marcus Tedesco
Can Neo4j graph databases and Python help us understand climate policy? Find out!
Observability for Distributed Computing with Dask
Hendrik Makait
Debugging is hard. Distributed debugging is hell. Let’s dive into distributed logging, automated metrics, event-based monitoring, and root-causing problems with diagnostic tooling to understand how Dask helps you remain sane while identifying and solving your problems.
Pandas 2.0 and beyond
Joris Van den Bossche, Patrick Hoefler
Pandas has reached a 2.0 milestone in 2023. But what does that mean? And what is coming after 2.0? This talk will give an overview of what happened in the latest releases of pandas and highlight some topics and major new features the pandas project is working on.
Polars - make the switch to lightning-fast dataframes
Thomas Bierhance
Want to learn about a new Python library that can speed up your datascience and analytics work? Join us at the conference to hear about polars, a lightning-fast dataframe library based on Apache Arrow and written in Rust!
Practical Session: Learning on Heterogeneous Graphs with PyG
Ramona Bendias, Matthias Fey
Building and learning on heterogeneous graphs with PyG in a practical session
Pragmatic ways of using Rust in your data project
Christopher Prohm
Pragmatic ways of using Rust in your data project - strategies to speed up your data pipelines without rewriting the whole program.
Raised by Pandas, striving for more: An opinionated introduction to Polars
Nico Kreiling
Have you also been raised with #pandas for all kinds of data transformations and wonder, if there is more? I did, I searched for performance and more concise syntax, and I would like to introduce you to #polars
Rusty Python: A Case Study
Robin Raymond
Talk on optimizing Python performance with Rust and PyO3, including case study, code profiling, and live demonstration of speedup. Discussion on PyO3 features and tradeoffs with other FFI options.
Shrinking gigabyte sized scikit-learn models for deployment
Pavel Zwerschke, Yasin Tatar
Shrinking gigabyte sized scikit-learn models for deployment: this talk shows how to deploy machine learning models with up to 6x disk space improvement
Specifying behavior with Protocols, Typeclasses or Traits. Who wears it better (Python, Scala 3, Rust)?
Kolja Maier
Did you ever wonder how to elegantly & safely abstract over concepts in your code? Check out Python's `typing.Protocol`, Scala's Typeclasses, and Rust's Traits!
Streamlit meets WebAssembly - stlite
Yuichiro Tachibana
Streamlit, a pure-Python data app framework, has been ported to Wasm as "stlite". See its power and convenience with many live examples and explore its internals from a technical perspective. You will learn to quickly create interactive in-browser apps using only Python.
The Battle of Giants: Causality vs NLP => From Theory to Practice
Aleksander Molak
Join us for a workshop on the latest advances in Causal NLP to see the Causal Transformer in action! All in Python! ❤️
The Beauty of Zarr
Sanket Verma
Hi all, I’ll be talking about Zarr, an open-source data format for storing chunked, compressed N-dimensional arrays, along with a hands-on session. If you work with huge datasets in local/cloud storage and looking for an efficient format, please attend my talk. Thanks!
The Spark of Big Data: An Introduction to Apache Spark
Pasha Finkelshteyn
Spark your big data skills! Learn Apache Spark basics: data frames, SQL APIs, and merging data for Python devs new to big data & tech explorers. Don't miss out! #ApacheSpark #BigData #Python
Thou Shall Judge But With Fairness: Methods to Ensure an Unbiased Model
Nandana Sreeraj
Biased models can impact each of us. While it may feel abstract, AI fairness can be achieved through many methods and metrics. More so, mitigation reports can initiate you to responsible AI. Check out my talk & demo at PyData Berlin.
Unlocking Information - Creating Synthetic Data for Open Access.
Antonia Scherz
A lot of data is private but this talk is not - learn how to synthesize anonymized, reliable data from sensitive, private data.
Use Spark from anywhere: A Spark client in Python powered by Spark Connect
Martin Grund
Check out how to participate in the extension of Spark Connect to bring the power of Spark everywhere!
Visualizing your computer vision data is not a luxury, it's a necessity: without it, your models are blind and so do you.
Chazareix Arnault
Visualizing your #ComputerVision data is not a luxury, it's a necessity: without it, your models are blind and so do you! Learn how to elevate your projects and #datasets with #DatasetVisualization.
What are you yield from?
Maxim Danilov
In this talk we will discover why many developers avoid using generators in regular python code.
Writing Plugin Friendly Python Applications
Travis Hathaway
Learn how to write plugin friendly applications with Python with the pluggy library!
“Who is an NLP expert?” - Lessons Learned from building an in-house QA-system
Nico Kreiling, Alina Bickel
Imagine to have somethingn like ChatGPT for your worklife! Or at least a bot you could ask about all your internal documents? We tried to build something like that @scieneers and will tell you about our journey #haystack #weaviate
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