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.

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.

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 .

Content Recommendation with Graphs: From Basic Walks to Neural Networks
Dr. Mirza Klimenta

Content Recommendation with Graphs: From Basic Walks to Neural Networks

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).

From idea to production in a day: Leveraging AzureML and Streamlit to build and user test machine learning ideas quickly
Florian Roscheck

How to leverage AzureML, 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.

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.

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.

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!

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

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

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.

Select ML from Databases
Gregor Bauer

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

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.

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.

That’s it?! Dealing with unexpected data problems
Simon Pressler

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

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

Using ML to find out the "Why"? A Tutorial in Causal Machine Learning
Philipp Bach

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

Your Model _Probably_ Memorized the Training Data
Katharine Jarmul

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

🌳 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 🛰️