Tomáš Neubauer

Introducing an open source library in Python: Quix Streams. It solves all the complexities of stream processing in a cloud native package with a familiar Pandas DataFrame API interface. This library lets you work with data like they are static in your Jupyter Notebook without any hassle...

In this talk, we will explore common problems faced when building real-time applications at scale, with a focus on a specific use case: detecting and responding to cyclist crashes.

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 case study offers an entertaining way to learn about the possibilities of stream processing, which can be applied to projects in fields that require easy access to current information, such as finance, mobility and energy.