Forecasting at Scale and Distributed Data Processing with Ray

Explore cutting-edge techniques for large-scale machine learning in this conference talk featuring two exclusive Ray community user presentations. Learn how to leverage Ray for crunching time-series and forecasting data at massive scale with Nixtla, and discover distributed data processing using Python Daft DataFrames with Ray as the compute engine. Begin with welcome remarks and Ray announcements from Jules Damji of Anyscale, followed by a 30-35 minute talk on "Forecasting at Scale with Nixtla and Ray" by Max Mergenthaler and Frederico Ramirez from Nixtla. Conclude with a 30-35 minute presentation on "Daft: The Ray-native Python DataFrame for Complex Data" by Jay Chia from Eventual. Gain valuable insights into Ray use cases and implementation strategies for ML practitioners seeking to enhance their data processing capabilities.