Simplifying Real-Time ML Pipelines with Quix Streams: An Open Source Python Library for ML Engineers

As data volume and velocity continue to increase, the need for real-time machine learning (ML) is becoming more pressing. However, building real-time ML pipelines can be complex and time-consuming, requiring expertise in both ML and streaming application development.

This talk will address this problem by introducing Quix Streams, an open-source Python library that makes it easy for data scientists and ML engineers to build real-time ML pipelines without having to learn the intricacies of building a streaming application from scratch.

In this talk, we’ll cover:

  • The growing importance of real-time ML in today's application stack, and the use cases for real-time ML processing.
  • A comparison of different ML architectures (batch, request-response, stream, and hybrid) and their pros and cons
  • The current state of streaming architecture, which is typically Java-based, and the challenges this poses for data scientists and ML engineers who primarily work in Python
  • An overview of Quix Streams and its features, including a demo of how to use it to build real-time ML pipelines

This talk is relevant for data scientists, ML engineers, and software engineers who are looking to adopt new technologies and practices in order to build real-time ML pipelines and stay current in their field.

What's the focus of your work these days?

I work as a technical authority for the engineering team here at Quix and I’m responsible for the direction of the company across the full technical stack. On top of that, I’m often on the road speaking at conferences and meetups about enabling data teams to do stream processing.

What's the motivation for your talk at QCon London 2023?

It’s traditionally been very difficult for data teams to do stream processing. It’s something I saw first-hand working at McLaren F1. Enabling Data Scientists to work directly with the product using ML/AI has the potential to revolutionalize next-gen products. The current streaming stack is not built to help with this transition to empower data teams and that’s the driving motivation behind open-sourcing Quix Streams and this talk.

How would you describe your main persona and target audience for this session?

Senior data scientists, software engineers and ML engineers.

Is there anything specific that you'd like people to walk away with after watching your session?

I'd like them to walk away with an idea of how to build next-gen real-time products powered by ML.


Tomáš Neubauer

CTO & Co-Founder @Quix

Tomáš Neubauer is a co-founder and the CTO at Quix, works as a technical authority for the engineering team and is responsible for the direction of the company across the full technical stack. He was previously technical lead at McLaren, where he led architecture uplift for Formula 1 racing real-time telemetry acquisition. He later led platform development outside motorsport, reusing the know-how he gained from racing.

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Find Tomáš Neubauer at:


Wednesday Mar 29 / 04:10PM BST ( 50 minutes )


Mountbatten (6th Fl.)


python library Machine Learning real time open source


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