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.
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
Shirli Di-Castro Shashua
Excited to share my journey at the intersection of healthcare and AI! Join me as I unveil the game-changing 'chatting with my medical database' feature, powered by LLMs. Discover how AI, SQL, and GraphQL team up to revolutionize doctors' access to crucial patient data.
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!
Exploring the need for fairness in machine learning in indirect human impact areas, proposing solutions for challenges in unstructured data.
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.
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 .
Meet Vocode, an open-source framework for AI voice agents. We'll cover its integration of speech APIs, LLMs, and conversation etiquette in real-world applications. #OpenSource #AI #VoiceAgents
Can ChatGPT convince you to get a COVID19 vaccine? Comparing ChatGPT to an expert system - which one is more convincing?
Dr. Lisa Andreevna Chalaguine
Comparison between ChatGPT and Domain-Specific Expert System - which is more convincing in getting people to vaccinate against COVID-19?
Dr. Mirza Klimenta
Content Recommendation with Graphs: From Basic Walks to Neural Networks
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.
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).
Hi all! I’ll discuss Zarr, an open-source data format for storing chunked, compressed N-dimensional arrays. We’ll explore the Zarr ecosystem from fundamentals to V3.0 and beyond. If you’re interested in storing massive datasets, please attend my talk. Thanks!
Flix CitySnap: How we use GenAI and not only to collect captivating images for cities and confirm their locations
Flix’s buses operate in more than 5,000 cities around the world. In this talk, we will demonstrate how we leverage state-of-the-art models, including Generative AI, to automatically collect images for thousands of cities and verify their locations. Our comprehensive end-to-end pi
From idea to production in a day: Leveraging AzureML and Streamlit to build and user test machine learning ideas quickly
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.
From LLM as oracle to LLM as translator - our journey from theory to everyday’s practice in a corporate setting with dmGPT (and python)
Emma Haley, Niklas Lederer
Learn how dm-drogeriemarkt put LLMs in production and implemented a day-to-day assistant for everyone.
Uwe L. Korn
Only ever used pandas.to_parquet? Would you like to know what it does and how you could make it even more efficient? Find out about Parquet's newest features in this talk.
Building a high performance streaming data website with Perspective
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.
RAG handles common issues in LLM applications, but a dependable system requires one more step: a fallback mechanism. Explore the implementation of LLM applications with diverse fallback techniques using Haystack in Bilge's insightful talk.
Learn how agent-based reasoning can improve baseline performance on LLMs on math benchmarks by 30%.
Marc Palyart, Kateryna Budzyak
GenAI is sometimes touted as the panacea for all natural language processing (NLP) tasks. This presentation explores a practical text classification scenario at Malt, highlighting the practical hurdles encountered when employing GenAI and how we overcame these obstacles.
Jupyter Notebooks as a platform to create books, magazine and newspaper articles, and other print media
Cement is responsible for about 8% of worldwide carbon emissions. Let me tell you about lessons learned decarbonizing the industry with Machine Learning.
Learn to read equations like an engineer and lose your fear of math
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.
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!
"Discover PyFixest, a Python library inspired by R's 'fixest'! 🐍📊 It speeds up regression model estimation with high-dimensional fixed effects, offering tools for robust inference and efficient post-processing. Perfect for AB Tests and event studies! #Python #DataScience #PyDat
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
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.
Florian Jetter, Patrick Hoefler
Dask DataFrame is fast now - The re-implementation of DataFrames in Dask is fast, reliable and fun.
Marcel Kurovski, Steffen Klempau
Personalizing Carousel Ranking on Wolt's Discovery Page with a Hierarchical Multi-Armed Bandit Approach
Learn how to use Polars for time series: what it does, and it doesn't do (and what to do about that!)
Put your RAG to the test: Component-per-component evaluation of our LLM-powered airplane manufacturing assistant
This talk discusses the topic of component-wise evaluation of RAG-based applications on the example of a digital shopfloor assistant developed at Airbus using open source Python libraries paired with Google Vertex AI.
While developing a Proof-Of-Concept RAG is widely accessible, creating a performant version that truly adds value remains a challenge. We willl share our learnings from building a RAG for a medical company, aiding doctors with drug documentation.
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.
Safeguarding Privacy and Mitigating Vulnerabilities: Navigating Security Challenges in Generative AI
How to protect and secure your data will using LLM and Generative AI. Your data privacy and security is importance.
Select ML from Databases: New workflow for building your machine learning models using the capabilities of modern databases
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.
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?! How to deal with unexpected data quality and quantity issues
Are we heading further into a black box era with larger and larger models, obscured behind APIs controlled by big tech monopolies? I don’t think so, and in this talk, I’ll show you why.
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
Learn to make practical decisions in data engineering with Python's vast ecosystem. Avoid blindly following market guidelines and consider the reality of your situation for better performance and architecture
Anuun, Hiba Jamal
A new wave in data engineering! From tangled tasks to sleek, plug-and-play magic in data pipelines. 🚀
This talk demonstrates how we can intuitively analyze political debates using knowledge graphs created using LLMs.
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
🕵️ 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.
Would you rely on ChatGPT to dial 911? A talk on balancing determinism and probabilism in production machine learning systems
Nicolas Guenon des Mesnards
Combining deterministic and probabilistic models to boost ML system robustness. Learn their benefits and applications in AI, backed by NLP case studies. #AIInnovation #MLTech #RobustAI
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 🛰️
🌳 The taller the tree, the harder the fall. Measuring tree height from space using Deep Learning 🛰️