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Webinar series

Real-time Machine Learning: Architecture and Challenges

October 25, 2022 | 9:00 AM - 9:45 AM Pacific Time

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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.

In this webinar you will learn
  • 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.

Presented by

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Chip Huyen

Co-founder of Claypot AI

About the presenter

Chip Huyen

​​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).

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We look forward to seeing you!

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