Machine Learning at Scale Using Distributed Stream Processing
Details
Hi AustinJUG Members,
We're going to have a virtual meeting next Tuesday night from 7 PM to 9 PM featuring talks by Hazelcast's senior solutions architect, Mike Yawn, on creating scalable machine learning solutions using distributed stream processing. Our own Gil Pratte will also give a short overview talk of the SpringOne 2020 conference.
Please register for the Zoom meeting here:
https://hazelcast.zoom.us/meeting/register/tJYlce-gqDotGt37poyxueEGiuNzKwQqPY6O
Hope to see you online next Tuesday night!
Rob
-------------------------Meeting Details------------------------------
Technotizer
Overview of the SpringOne 2020 Conference
by Gil Pratte - https://www.linkedin.com/in/gil-pratte/
Main Talk
Machine Learning at Scale Using Distributed Stream Processing
There is frequently an "impedance mismatch" between developing and training a machine learning module (a data scientist's job) and then making that model deploy and perform at scale in a production environment (a data engineer's job). How do you make a trained prediction model usable in real-time, while the user is interacting with your software? What does it take to go from fast trial-and-error runs on historical data to models that perform at production scale, in real-time?
In this talk we will demonstrate a practical approach that allows you to write a low-latency, auto-parallelized, and distributed stream processing pipeline (Java), using a model developed in Python.
About Mike Yawn
Mike Yawn is a Senior Solutions Architect with Hazelcast, the provider of the leading operational In-Memory Computing Platform. In that role, he provides pre-sales consulting on Hazelcast IMDG, Hazelcast Jet, and Hazelcast Cloud solutions to commercial customers. Prior to joining Hazelcast, Mike performed a number of consulting and R&D functions with HP, eBay, Oracle, and EMC, supporting customers in manufacturing, banking, healthcare, and other industries.