Mike Kraus
I am a hybrid machine learning engineer, data scientist and cloud infrastructure engineer whose professional track record covers many aspects of applied data science, MLOps and (big) data engineering. I am passionate about developing scalable, production-ready, and efficient ML applications / infrastructure.
I relate to the everyday struggles that data scientists and machine learning engineers encounter in their workflows, whether that be reproducible experimentation, feature engineering, model training, or inference. Being able to come up with solutions for impediments in these areas and enabling data science teams to be as productive as possible is what drives me.
Sessions
Python is beloved for its simplicity and versatility, but it can struggle with performance in compute-intensive tasks. Rust, on the other hand, offers high performance and memory safety. This talk will explain how you can harness the power of Rust to enhance Python modules using the PyO3 library.
We will explore this through a practical example: a pure Python payment handler and an optimized version where its functionality is abstracted away using Rust. This approach will demonstrate how to overcome performance bottlenecks while retaining the ease of use and flexibility that Python offers. However, like any tool, it comes with its own considerations and trade-offs.
This talk is particularly interesting for Machine Learning Engineers and Python developers seeking to boost the performance of their applications.