Meet the Expert: Dean Wampler on Scaling ML/AI Applications with Ray
Modern ML and AI applications require a lot of compute power, which usually means distribution over a cluster of machines, as well as management of distributed state, such as the model parameters being trained. Ray, a high-performance distributed execution framework developed by UC Berkeley’s RISELab, is targeted at large-scale machine learning and reinforcement learning applications. Ray’s features make it suitable for any Python-based application that needs cluster-wide scalability.
Join us for this edition of Meet the Expert with Dean Wampler to see how Ray meets the needs of ML/AI applications—without requiring the skills and DevOps effort typically required for distributed computing. You’ll learn how Ray enables distribution of Python applications over a cluster and explore examples of ML libraries that use Ray, allowing data scientists to do their work at scale without a lot of programming.
O'Reilly Meet the Expert explores emerging business and technology topics and ideas through a series of one-hour interactive events. You’ll engage in a live conversation with experts, sharing your questions and ideas while hearing their unique perspectives, insights, fears, and predictions.
What you'll learn-and how you can apply it
By the end of this live show, you’ll better understand:
- The challenges of scaling Python-based applications, especially for ML/AI
- How Ray makes it much easier to scale these applications
- How to use Ray-enabled Python libraries for many ML tasks
- How to use Ray in your own Python applications, such as microservices
This Discussion is for you because...
- You want to learn about the fundamental architectural shifts in ML/AI and microservices that are hard to satisfy with traditional technologies.
- You want to learn how to meet those challenges with new systems like Ray.
- Come with your questions for Dean Wampler
- Have a pen and paper handy to capture notes, insights, and inspiration
About our guest
Dean Wampler is an expert in streaming systems, focusing on ML/AI. He’s head of developer relations at Anyscale.io, which is developing Ray for distributed Python. Previously, he was an engineering VP at Lightbend, where he led the development of Lightbend CloudFlow, an integrated system for streaming data applications with popular open source tools. Dean has written books for O'Reilly and contributed to several open source projects. He’s a frequent conference speaker and tutorial teacher and is a co-organizer of several conferences and user groups in Chicago. Dean has a PhD in physics from the University of Washington.
The timeframes are only estimates and may vary according to how the class is progressing
Monday, October 5, 2020, at 9:00am PT / 12:00pm ET
- Introduction and presentation (15 minutes)
- Interactive discussion and Q&A (45 minutes)