Real-time Machine Learning: Architecture and Challenges
October 25, 2022 | 9:00 AM - 9:45 AM Pacific Time
Fresh data beats stale data for machine learning applications. This webinar will discuss the value of fresh data as well as different types of architecture and challenges of online prediction, it will also cover the tradeoffs between latency, staleness, and cost.
- Use cases for real-time ML.
- Architectures well suited for online predictions taking feature computation, prediction, and request response times into consideration.
- How to overcome key challenges with online prediction systems such as latency vs. feature freshness, accuracy, and streaming infrastructure management.
Plus, the first 200 participants who join the live event will receive a free Kindle edition of Chip’s book designing Machine Learning Systems which covers a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements sent by email afterward.
Co-founder of Claypot AI
About the presenter
Chip Huyen is a co-founder of Claypot AI, a platform for real-time machine learning. Previously, she was with Snorkel AI and NVIDIA. She teaches CS 329S: Machine Learning Systems Design at Stanford. She’s the author of the book Designing Machine Learning Systems (O’Reilly, 2022).