Overcoming Distributed ML Challenges with Ray Train | Ray Summit 2024

Video Description

In this Ray Summit 2024 breakout session, Matthew Deng from Anyscale tackles the complexities of distributed machine learning. This talk delves into the key challenges faced by ML practitioners, including setup difficulties, integration issues, and debugging complexities in distributed environments. Deng explores solutions to streamline ML workflows, focusing on the Ray Train API and its ecosystem integrations. The presentation covers: - Simplifying distributed ML setup processes - Integrating Ray Train with existing ML workflows - Enhancing observability for easier debugging - Implementing fault tolerance to reduce resource waste - Efficient data processing for large-scale datasets using Ray Data Watch this breakout session to gain practical insights to enhance the efficiency and scalability of your distributed ML pipelines. This session offers valuable knowledge for data scientists and ML engineers looking to optimize their training processes at scale. -- Interested in more? - Watch the full Day 1 Keynote: https://youtu.be/jwZHJthQvXo - Watch the full Day 2 Keynote https://youtu.be/Lury2ad6KG8 -- 🔗 Connect with us: - Subscribe to our YouTube channel: https://www.youtube.com/@anyscale - Twitter: https://x.com/anyscalecompute - LinkedIn: https://linkedin.com/company/joinanyscale/ - Website: https://www.anyscale.com