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.

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.

Bayesian Marketing Science: Solving Marketing's 3 Biggest Problems
Dr. Thomas Wiecki

A Bayesian modeling toolkit to solve today's biggest marketing challenges.

Bringing NLP to Production (an end to end story about some multi-language NLP services)
Larissa Haas, Jonathan Brandt

How to bring NLP models to production? Following a use case that runs for over 1 year in 10 different languages, this talk will enable you to ask the right questions before starting to deploy NLP services.

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.

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

Enabling Machine Learning: How to Optimize Infrastructure, Tools and Teams for ML Workflows
Yann Lemonnier

Join us for a deep dive into machine learning enabler engineering! Discover how to optimize infrastructure, tools and teams for ML workflows and reduce time to deployment. Get practical tips and insights for successful projects from an expert in the field. #MLenabler #optimizingM

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!

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

Great Security Is One Question Away
Wiktoria Dalach

Security doesn't have to be a nightmare. The 3rd hack will surprise you.

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

How to baseline in NLP and where to go from there
Tobias Sterbak

Join us for a talk on baselines in NLP! We'll cover common tasks like classification, clustering, search, and NER, and discuss how to establish and improve baselines using weak learning. Don't miss out on this opportunity to gain a deeper understanding of NLP baselines!

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

Hyperparameter optimization for the impatient
Martin Wistuba

HPO does not need to be expensive, see how to speed it up with a couple of simple algorithms

Improving Machine Learning from Human Feedback
Erin Mikail Staples, Nikolai

While powerful, models built off large datasets like GPT-3 often bring their biases along with them. However, is this the best future for machine learning? Join us to explore Reinforcement Learning from Human Feedback (RLHF) techniques and why they matter more now than ever.

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!

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

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

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.

Performing Root Cause Analysis with DoWhy, a Causal Machine-Learning Library
Patrick Blöbaum

Learn how to use the Python DoWhy library to perform root cause analysis using methods of causal machine-learning.

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!

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.

Software Design Pattern for Data Science
Theodore Meynard

I will share some specific software design concepts that can be used by data scientists to build better data products.

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!

Staying Alert: How to Implement Continuous Testing for Machine Learning Models
Emeli Dral

ML monitoring might be easy for a single model, but hard at scale. In this talk, I will introduce the idea of test-based monitoring, and how to standardize data and model checks across models and lifecycle.

Teaching Neural Networks a Sense of Geometry
Jens Agerberg

By taking neural networks back to the school bench and teaching them some elements of geometry and topology we can build algorithms that can reason about the shape of data. This is the promise of the emerging field of Topological Data Analysis (TDA) which we will introduce!

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 CPU in your browser: WebAssembly demystified
Antonio Cuni

WebAssembly is essentially a virtual and efficient CPU embedded in your browser. Let's see what it is!

The State of Production Machine Learning in 2023
Alejandro Saucedo

Join us at the PyCon DE conference to learn about the current state of production machine learning in the Python ecosystem! We'll cover key principles, frameworks for end-to-end ML lifecycle, best practices, and recommended tools for deployment, security, and scaling.

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!

Using transformers – a drama in 512 tokens
Marianne Stecklina

Nearly all pretrained transformers have an annoying limitation: they can only process short input sequences. Watch me rant about it ;-)

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.

WALD: A Modern & Sustainable Analytics Stack
Florian Wilhelm

WALD: A modern & sustainable analytics stack consisting of a warehouse like Snowflake or BigQuery, Airbyte, Lightdash and dbt.

What are you yield from?
Maxim Danilov

In this talk we will discover why many developers avoid using generators in regular python code.

Why GPU Clusters Don't Need to Go Brrr? Leverage Compound Sparsity to Achieve the Fastest Inference Performance on CPUs
Damian Bogunowicz

Fun fact: you can remove 90% of a neural network's weights without losing much accuracy! With model sparsity, you can even run these networks on your CPU with GPU-level performance. Learn about compound sparsity (pruning, quantization, knowledge distillation) for faster inference

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