Reactive Machine Learning

Bringing reactive principles to big data's killer app

Blogging

I regularly post my latest ideas around reactive machine learning on the data engineering collection on Medium. The original post that kicked off reactive machine learning, can be found here, although the ideas have evolved significantly since then. So the book should be considered the canonical record of what reactive machine learning is all about.

The Book

I'm writing Reactive Machine Learning Systems for Manning. It's currently in early access, so chapters are becoming available as I write them. The book is all about the ideas behind reactive machine learning and how to use awesome tools like Scala, Akka, and Spark to build reactive machine learning systems.

Talks

  • Spark as the Gateway Drug to Typed Functional Programming
  • Reactive for Machine Learning Teams
  • Reactive Feature Generation with Spark and MLlib
  • Bringing Data Scientists and Engineers Together-video
  • At the Edge of AI
  • Collecting Uncertain Data the Reactive Way-video
  • Reactive Machine Learning On and Beyond the JVM-video
  • Reactive Machine Learning and Functional Programming-video
  • Spark for Reactive Machine Learning: Building Intelligent Agents at Scale
  • Characterizing Intelligence with Elixir
  • Reactive Learning Agents
  • Introducing Reactive Machine Learning

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About Me

My name is Jeff Smith. I build artificial intelligence systems. Over the past decade or so, I’ve written all sorts of data-centric systems for startups in New York City, San Francisco, and Hong Kong. Most of my thoughts on how to build systems that reason about data at scale wind up on Medium. When I’m not diving deep into data architectures, I’m usually spending too much time on Twitter.