Real-Time Streaming with Python ML Inference

UPDATE:
We had to cancel the originally promoted topic of Chandy-Lamport algorithm for this time. The speaker, unfortunately, is not coming to Brno.
But don't be sad!
First, we are not canceling the topic completely. We are just postponing it to some of the next meetups.
Second, we have got a great replacement and we are going to deep dive to stream processing.

The capabilities of machine learning are now pretty well understood and there are great tools to do data science and construct models that answer nontrivial questions about your data. These tools are mostly used in Python.

The key new challenge is making the trained prediction model usable in real-time, while the user is interacting with your software. Getting answers from an ML model (this is called inference) takes a lot of CPU and must be done at serious scale. The ML tools are optimized mainly for batch-processing a lot of data at once, and often the implementations aren't parallelized.

In this talk, I will show one approach which allows you to write a low-latency, auto-parallelized and distributed stream processing pipeline in Java that seamlessly integrates with a data scientist's work taken in almost unchanged form from their Python development environment.

The talk includes a live demo using the command line and going through some Python and Java code snippets.

The speaker is Marko Topolnik (https://hazelcast.com/blog/author/markotopolnik/), the senior developer of Hazelcast Jet.

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The presentation will be in English.

Don't hesitate to ask any questions.