PyData: Machine Learning & Deep Learning & Stats

Talk pydata-machine-learning-deep-learning-stats

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.

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Breaking AI Boundaries: Fairness Metrics in Unstructured Data Domains

Daniel Klitzke

Exploring the need for fairness in machine learning in indirect human impact areas, proposing solutions for challenges in unstructured data.

Tutorial pydata-machine-learning-deep-learning-stats

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 .

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Content Recommendation with Graphs: From Basic Walks to Neural Networks

Dr. Mirza Klimenta

Content Recommendation with Graphs: From Basic Walks to Neural Networks

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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).

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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.

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Lessons learned from deploying Machine Learning in an old-fashioned heavy industry

Robert Meyer

Cement is responsible for about 8% of worldwide carbon emissions. Let me tell you about lessons learned decarbonizing the industry with Machine Learning.

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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.

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Missing Data, Bayesian Imputation and People Analytics with PyMC

Nathaniel Forde

Hierarchical structures are everywhere in business! Ever wondered how trickle-down management missteps drive non-response bias in Employee Engagement? Model the hierarchy, model the missing-ness with PyMC!

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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

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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

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Reinforcement Learning: Bridging The Gap Between Research and Applications

Michael Panchenko

Reinforcement learning (RL) has untapped potential for industry. This talk presents Tianshou, an open-source library with interfaces facilitating both industrial RL applications and new algorithm research, with the dual goals of accelerating progress and adoption.

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Select ML from Databases

Gregor Bauer

Select ML from Databases: New workflow for building your machine learning models using the capabilities of modern databases

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Tackling the Cold Start Challenge in Demand Forecasting

Alexander Meier, Daria Mokrytska

Exploring the Cold Start problem in Demand Forecasting. Overcoming difficulties faced by Time Series and ML models. Uncover practical techniques and a systematic evaluation framework for effective forecasting.

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Tailored and Trending: Key learnings from 3 years of news recommendations

Dr. Christian Leschinski

Diving into the world of recommendations! Learn how we overcome the special challenges of recommending news at Axel Springer NMT by using simple statistics.

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That’s it?! Dealing with unexpected data problems

Simon Pressler

That’s it?! How to deal with unexpected data quality and quantity issues

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The evolution of Feature Stores

Olamilekan Wahab

Feature Stores have become an important component of the machine learning lifecycle. They have been particularly pivotal in bridging the gap between data engineering and machine learning workflows(experimentation, training and serving). This talk will explore Feature Stores with

Tutorial pydata-machine-learning-deep-learning-stats

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

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Your Model _Probably_ Memorized the Training Data

Katharine Jarmul

So, just how much data did ChatGPT memorize? Let's find out!

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🌳 The taller the tree, the harder the fall. Determining tree height from space using Deep Learning and very high resolution satellite imagery 🛰️

Ferdinand Schenck

🌳 The taller the tree, the harder the fall. Measuring tree height from space using Deep Learning 🛰️