04-22, 15:35–16:05 (Europe/Berlin), A1
The foundations of machine learning were built on offline batch processing techniques for model training and inference. As organisations become more dependent on real-time data, the technological trend for machine learning in production is moving towards adding an online stream processing approach. This has benefits such as lower computational requirements due to being able to incrementally learn from a stream of data points, which enables the continual upgrading of models by adapting to real-time changes in data. Learn how to get started on your online ML journey with River
The foundations of machine learning were built on offline batch processing techniques for model training and inference. As organisations become more dependent on real-time data, the technological trend for machine learning in production is moving towards adding an online stream processing approach. This has benefits such as lower computational requirements due to being able to incrementally learn from a stream of data points, which enables the continual upgrading of models by adapting to real-time changes in data.
This has wide applications in industries such as cyber security, banking, healthcare, IIoT and any industry that involves processing large volumes of high throughput data and adapting predictive capability with real-time data feeds.
You’ll leave this talk with an understanding of the differences between offline and online machine learning, how to complement one with the other and enough streaming concepts and best practices needed get started on your online ML journey with River, an open source Python ML library.
Novice
Expected audience expertise: Python –Intermediate
Abstract as a tweet (X) or toot (Mastodon) –Learn the differences between online and offline ML and get started on your online ML journey today with River, an open source Python ML library
Tun Shwe is the VP of Data at Quix, where he leads data strategy and developer relations. He is focused on helping companies imagine and implement their strategic data vision with stream processing at the forefront. He was previously a Head of Data and Data Engineer at high growth startups and has spent his career leading T-shaped teams in developing analytics platforms and data-intensive AI applications.
In his spare time, Tun goes surfing, plays guitar and tends to his analogue cameras.