Update Your Model's View Of The World In Real Time With Streaming Machine Learning Using River

The Python Podcast.__init__
The podcast about Python and the people who make it great
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This is a cross-over episode from our new show The Machine Learning Podcast, the show about going from idea to production with machine learning.
The majority of machine learning projects that you read about or work on are built around batch processes. The model is trained, and then validated, and then deployed, with each step being a discrete and isolated task. Unfortunately, the real world is rarely static, leading to concept drift and model failures. River is a framework for building streaming machine learning projects that can constantly adapt to new information. In this episode Max Halford explains how the project works, why you might (or might not) want to consider streaming ML, and how to get started building with River.
The intro and outro music is from Hitman’s Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0
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Introduction to the Episode
Guest Introduction: Max Halford
The River Project: Origin and Purpose
Online vs. Batch Machine Learning
Use Cases for Online Machine Learning
Model Per User Concept
Operational Model for Streaming ML
Design and Implementation of River
State Management in River
Building and Deploying Models with River
Challenges in Adopting Online Learning
Interesting Use Cases of River
When Not to Use River
Future Plans for River and Beaver
Biggest Barriers to ML Adoption
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