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

<div class="wp-block-jetpack-markdown"><h2>Preamble</h2> <p>This is a <a href="https://www.themachinelearningpodcast.com/river-streaming-machine-learning-episode-8/?utm_source=rss&utm_medium=rss" target="_blank" rel="noopener">cross-over episode</a> from our new show <a href="https://www.themachinelearningpodcast.com?utm_source=rss&utm_medium=rss" target="_blank" rel="noopener">The Machine Learning Podcast</a>, the show about going from idea to production with machine learning.</p> <h2>Summary</h2> <p>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, wh...