Tools for Making Machine Learning more Reactive

May 3, 2018 · New York, United States of America

The world of machine learning tooling has advanced leaps and bounds over the past several years. Powerful Scala tools like Spark, Akka, and Kafka are being successfully applied to the challenges of building large scale machine learning systems. Reactive systems design is finally coming to the world of machine learning.

This story isn’t just about Scala, though. Lots of machine learning development is being done in Python, so a reactive machine learning system needs to work well with important Python tools like TensorFlow. Even further afield, the ancient reactive technology of Erlang is making a big comeback, thanks to systems being built in Elixir, a new EVM language.

This talk gives a broad overview of how various machine learning components are being built using a variety of different tools. The focus will be on the strengths and differences between various technologies from Scala, Python, and Erlang/Elixir, as tools to implement common machine learning components.

The speaker, Jeff Smith, is an AI developer, author, and manager. He is the author of Reactive Machine Learning Systems and the developer of various machine learning tools. He can be found at or on Twitter as @jeffksmithjr.

We are looking forward to seeing you all on Thursday, May 3rd!

If you RSVP'd Yes but can't make it please change your response to No. The number of spaces is limited and we want everyone to have a chance to attend!

Event organizers
  • Reactive New York

    A group bringing Reactive Application developers together. Focuses on all aspects of building modern Elastic, Responsive, Resilient and Message Driven applications. Including topics such as Reactive Programming, Akka, Scala, Functional Programming, Concurrent, Asynchronous and Parallel programming, Distributed Systems, SQL, NoSQL, NewSQL and distributed databases such as Cassandra, MongoDB, Riak etc., Large Scale Data Processing such as Apache Spark, Apache Flink, Apache Kafka etc., Stream Processing, Inte

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