Python Skill Level Intermediate Session List
A conceptual and practical introduction to Hilbert Space Gaussian Process (HSGP) approximation methods
Dr. Juan Orduz
In this talk, we explore a new method to approximate Gaussian processes using spectral analysis methods, known as the Hilbert Space Gaussian process (HSGP) approximation.
A deep dive into the Arrow Columnar format with pyarrow and nanoarrow
Joris Van den Bossche, Raúl Cumplido, Alenka Frim
Apache Arrow has become a de-facto standard for efficient in-memory columnar data representation. But what is this format exactly? This tutorial will dive deep into the details of the Arrow columnar format and explore interactively the different types and buffer layouts.
A Retrieval Augmented Generation system to query the scikit-learn documentation
Guillaume Lemaitre
A Retrieval Augmented Generation system to query the scikit-learn documentation
Async Awaits: Mastering Asynchronous Python in FastAPI
Bojan Miletic
Gear up for 'Async Awaits: Mastering Asynchronous Python in FastAPI' 🚀. Discover the power of async/await in #Python and learn how to supercharge your web apps with #FastAPI. Perfect for developers eager to excel in modern web development! 🌐 #TechTalk #AsyncPython #WebDev
Better search relevance using Learning to Rank at mobile.de
Manish Saraswat
This talk will discuss our current search relevance ranking framework and how it ranks millions of searches daily.
Boost your Data Science skills with the new Python in Excel
Valerio Maggio
Do you know that you can now run Python directly into Excel ? Come to my tutorial to know more, and to boost your data analytics skills!
Build a personalized Bitcoin (BTC) virtual assistant in Python with Hopsworks and LLM function calling
Javier de la Rúa Martínez
TDB ------------------------------------------------------------------------------------
Build an AI Document Inquiry Chat with Offline LLMs
Pavithra Eswaramoorthy, Philip Meier
In this hands-on tutorial, we'll build an LLM-powered document inquiry chat application that uses Retrieval-Augmented Generation (RAG) for more accurate results. We'll test different LLMs, run an offline LLM on GPUs, and demonstrate a fully functional web app.
Build TikTok's Personalized Real-Time Recommendation System in Python with Hopsworks
Jim Dowling
The real-time recommendations engine, Monolith, in Tiktok is so good it has been described as "digital crack". In 1 hr, we will build Monolith in Python as 3 ML pipelines that run on Hopsworks .
Bulletproof Python - Property-Based Testing with Hypothesis
Michael Seifert
Less is more! Rather than working harder and write more test code, property-based testing forces you to work smarter and cover more cases with fewer tests. Join Michael for his Tutorial "Bulletproof Python - Property-Based Testing with Hypothesis"
Cloud? No Thanks! I’m Gonna Run GenAI on My AI PC
Adrian Boguszewski, Dmitriy Pastushenkov
Join this talk to learn that cloud is no longer needed for GenAI. All you need is an AI PC.
Content Recommendation with Graphs: From Basic Walks to Neural Networks
Dr. Mirza Klimenta
Content Recommendation with Graphs: From Basic Walks to Neural Networks
Data valuation for machine learning
Miguel de Benito Delgado
pyDVL is the library for data valuation in machine learning. Use it to clean, prune and select your data to improve model performance.
DDataflow: An open-source end-to-end testing framework for ML pipelines
Theodore Meynard, Jean Machado
Explore our talk on DDataflow, a tool transforming ML pipeline testing. It streamlines the process with decentralized data sampling, tackling prolonged development and latent errors in large, complex datasets.
Deploying your Python application to Android
Shyamnath Premnadh
Current state of deploying Python applications to Android with a comparison of the current available frameworks.
Django loves strawberries
Arthur Bayr
Explore the dynamic duo of GraphQL Strawberry and Django in an immersive workshop! Discover the seamless integration of Strawberry with Django, mastering type definitions, queries and mutations.
Documenting R&D Progress using jupyter-book - and feel safe for the next performance audit
Jens Nie
Rosenxt has just been founded, yet we're already very busy to create the next best thing. Let me show you how we create our R&D progress using the jupyter-book ecosystem to be safe for the next performance audit.
Enhance your balcony power plant with Python
Jannis Lübbe
Improve the efficiency of your balcony power plant with Python
Everything you need to know about change-point detection
Charles Truong
How do you detect an activity change from smartwatch data, abrupt climate transitions, or server failures? If you work with long time series, you will inevitably have to detect changes. This talk describes how to do that using ruptures (https://github.com/deepcharles/ruptures).
Flix CitySnap: How we use GenAI and not only to collect captivating images for cities and confirm their locations
Andrei Chernov
Unlocking City Charisma: Leveraging Generative AI for Automated Image Collection and Elevated Customer Experience 🌟 Dive into Flix's innovative approach
From idea to production in a day: Leveraging Azure ML and Streamlit to build and user test machine learning ideas quickly
Florian Roscheck
How to leverage Azure ML, automated machine learning, and Streamlit to build and test machine learning apps quickly? Find out about our favorite Hackathon stack and walk away with some code to build and user-test your own machine learning ideas fast.
Functional Python
Mike Müller
Learn how to integrate functional features into your daily Python programming.
High Performance Data Visualization for the Web
Tim Paine
Building a high performance streaming data website with Perspective
How to Do Monolingual, Multilingual, and Cross-lingual Text Classification in April, 2024
Daryna Dementieva
If I want a text classifier in 2024, what should I choose -- LLMs or pre-LLM era classifier? Is the answer the same for English and other languages? We will provide the recipe how to find your classifier depending on the target language and data availability.
I achieved peak performance in python, here's how ...
Dishant Sethi
In the ever-evolving landscape of software development, crafting code that not only functions flawlessly but also operates at peak performance is a skill that sets exceptional developers apart. This talk delves into the art of optimizing Python code, exploring techniques and stra
Leveraging the Art of Parallel Unit Testing in Django
Azan Bin Zahid, Syed Ansab Waqar Gillani
Unlocking the power of parallel unit testing with Python and Django! 🚀
Machine Learning on microcontrollers using MicroPython and emlearn
Jon Nordby
Deploy ML models to microcontrollers - using just the Python you already know! A practical presentation on how to use the emlearn Machine Learning package and MicroPython to build smart sensor systems.
Marketing Media Mix Models with Python & PyMC: a Case Study
Emanuele Fabbiani
Discover how Italy's fastest-growing tour operator unlocked transformative marketing insights using Bayesian models, domain knowledge, Python, and PyMC. Gain valuable tips to develop similar models for your business.
Mojo 🔥 - Is it Python's faster cousin or just hype?
Jamie Coombes
"Chris Lattner's Mojo promised to revolutionize AI dev with 68k times speed & Python ease. One year later, we dissect its reality—can it outshine Rust & Julia, or is it just hype? #PyData #MojoLanguage #PythonCousin"
Moving from Offline to Online Machine Learning with River
Tun Shwe
Learn the differences between online and offline ML and get started on your online ML journey today with River, an open source Python ML library
Next Stop: Insights! How Streamlit and Snowflake Power Up Data Stories
Marie-Kristin Wirsching
Data stories are the bridge between complex data insights and business impact! Transforming data into clear, actionable narratives is no easy task. That's where Streamlit and Snowflake come in - a duo for creating visually engaging, interactive data applications.
Performant, scientific computation in Python and Rust
Stefan Ulbrich
A tutorial session on how to build scientific package for numerical calculus algorithm in Python and Rust.
Personalizing Carousel Ranking on Wolt's Discovery Page: A Hierarchical Multi-Armed Bandit Approach
Marcel Kurovski, Steffen Klempau
Personalizing Carousel Ranking on Wolt's Discovery Page with a Hierarchical Multi-Armed Bandit Approach
Polars and Time Series: what it can do, and how to overcome any limitation
Marco Gorelli
Learn how to use Polars for time series: what it does, and it doesn't do (and what to do about that!)
pytest tips and tricks for a better testsuite
Florian Bruhin
pytest lets you write simple tests fast - but also scales to very complex scenarios: Beyond the basics of no-boilerplate test functions, this training will show various intermediate/advanced features, as well as gems and tricks.
Python 3.12's new monitoring and debugging API
Johannes Bechberger
Python 3.12 got a new debugging and monitoring API. Learn in this talk why it will change debugging forever.
Python Monorepos: The Polylith Developer Experience
David Vujic
What if writing software would be more like building with LEGO bricks, and have a more playful developer experience. Polylith solves this in a nice and simple way. I’ll walk through the simple Architecture & the Developer friendly tooling for a joyful Python Experience.
Refactoring Large Programs
Dr. Kristian Rother
Refactor a large Python program that is undocumented, unstructured and untested
Replacing Callbacks with Generators: A Case Study in Computer-Assisted Live Music
Matthieu Amiguet
How we made our code more readable by replacing intricated callback-based code with much more readable generators. Also a great example of using python in an unexpected domain: realtime audio processing for live music!
Robust Configuration Management with Pydantic's Data Validation
Philipp Stephan
How Pydantic's strong data validation based on type annotations can help build a strict spec for your configuration format, catch misconfiguration early, and mitigate the aforementioned problems with a non-formalized configuration management system.
Securing Python: Race Condition Vulnerabilities
Shahriyar Rzayev
Explore and secure Python code against race condition vulnerabilities
Select ML from Databases
Gregor Bauer
Select ML from Databases: New workflow for building your machine learning models using the capabilities of modern databases
Streamlining Python Development: A Practical Approach to CI/CD with GitHub Actions
Artem Kislovskiy
Learn how continuous integration/delivery boosts project resilience to Python updates and packaging changes. Automate for peace of mind, better code, and seamless collaboration.
The key to reliability - Testing in the field of ML-Ops
Gunar Maiwald, Tobias Senst
idealo.de presents its holistic approach for testing in machine learning
The Struggles We Skipped: Data Engineering for the TikTok Generation
Anuun, Hiba Jamal
A new wave in data engineering! From tangled tasks to sleek, plug-and-play magic in data pipelines. 🚀
Unleashing Confidence in SQL Development through Unit Testing
Tobias Lampert
Confidently ship changes to your SQL data model by validating logic with a SQL unit testing framework. Our framework, powered by pytest, ensures robust deployments, making data model evolution a breeze.
Using ML to find out the "Why"? A Tutorial in Causal Machine Learning
Oliver Schacht, Jan Teichert-Kluge
Tutorial on Causal Machine Learning by the developers of the DoubleML package for Python. Learn how to address "Why?" questions with ML! https://docs.doubleml.org/stable/index.html #Causality #CausalML #DoubleML #CausalInference
Whispered Secrets: Building An Open-Source Tool To Live Transcribe & Summarize Conversations
John Sandall
🕵️ Calling all Spythonistas: Do you need a live speech transcription and summarization "secret agent" that works offline by running on your own hardware? Learn about the latest trends in open-source GenAI tools and how to build your own in this light-hearted talk.
You shall not pass! 🧙 Strengthen your python code against attacks.
Antonia Scherz, Roman Krafft
You shall not pass! Make your Python code strong against attacks.
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