Synthetic aviation fuels (SAFs) offer a pathway to improving efficiency, but high cost and volume requirements hinder property testing and increase risk of developing low-performing fuels. To promote productive SAF research, we used Fourier Transform Infrared (FTIR) spectra to train accurate, interpretable fuel property models. In this presentation, we will discuss how we leveraged standard Python libraries – NumPy, pandas, and scikit-learn – and Non-negative Matrix Factorization to decompose FTIR spectra and develop predictive models. Specifically, we will review the pipeline developed for preprocessing FTIR data, the ensemble models used for property prediction, and how the features correlate with physicochemical properties.

This talk explores various methods to accelerate traditional machine learning pipelines using scikit-learn, UMAP, and HDBSCAN on GPUs. We will contrast the experimental Array API Standard support layer in scikit-learn with the cuML library from the NVIDIA RAPIDS Data Science stack, including its zero-code change acceleration capability. ML and data science practitioners will learn how to seamlessly accelerate machine learning workflows, highlight performance benefits, and receive practical guidance for different problem types and sizes. Insights into minimizing cost and runtime by effectively mixing hardware for various tasks, as well as the current implementation status and future plans for these acceleration methods, will be provided.

Pandas and scikit-learn have become staples in the machine learning toolkit for processing and modeling tabular data in Python. However, when data size scales up, these tools become slow or run out of memory. Ibis provides a unified, Pythonic, dataframe-like interface to 20+ execution backends, including dataframe libraries, databases, and analytics engines. Ibis enables users to leverage these powerful tools without rewriting their data engineering code (or learning SQL). IbisML extends the benefits of using Ibis to the ML workflow by letting users preprocess their data at scale on any Ibis-supported backend.

In this tutorial, you'll build an end-to-end machine learning project to predict the live win probability after each move during chess games.