Blue Yonder at PyCon.DE 2023
It's been now 10 years ago when Blue Yonder started the first sponsoring of a python conference at Europython Florence. Since then we have been either sponsoring and/or organizing at least one python event per year.
Blue Yonder History
It has now been 10 years since Blue Yonder started its first sponsorship of a Python conference at EuroPython in Florence. Since then, we have been sponsoring or organizing at least one Python event per year. For me, this has always been part of my mission: to convince leadership and fellow team leads that participating in the open-source community benefits employee development and overall corporate culture. Young engineers learn to represent the company and connect with other open-source developers.
This year, PyCon.DE was hosted in Berlin. With 1,500 attendees, the conference has grown tremendously over the past years. The Berlin Congress Center is, of course, a very professional venue, and the organizing committee did an excellent job running a smooth conference overall. Still, I hope that PyCon.de will be hosted in another city next year (maybe even in Switzerland or Austria). Leipzig, Frankfurt, Hamburg, Basel, Bern, or Wien would be great locations for 2024.
Cyclic Boosting
The main topic of this year's Blue Yonder conference booth was the open-sourcing of Cyclic Boosting. Cyclic Boosting has been Blue Yonder’s core ML algorithm for many years. Felix Wick gave a talk about exploring the power of Cyclic Boosting: a pure-Python, explainable, and efficient ML method
We also hosted a small Kaggle ML/Retail Challenge where the open-source community can apply their ML algorithms to a typical retail problem and benchmark them against a baseline Cyclic Boosting model.
The challenge is to accurately forecast demand for 300 retail products across 20 retail stores. Accurate demand forecasting is crucial for retailers because it enables informed decisions regarding inventory management, pricing strategies, and sales projections, all of which can significantly impact the bottom line. In short, demand forecasting is essential for retailers aiming to optimize operations and maximize profits to stay competitive in a crowded retail landscape.
It was quite fun to discuss solutions and technical approaches at our booth, and people were highly engaged, trying to beat Felix’s reference implementation.
Notable PyCon.DE talks
Wald: A Modern and Sustainable Analytics Stack from Florian Wilhelm.
The name WALD stack comes from the four technologies it is composed of, i.e., a cloud data warehouse such as Snowflake or Google BigQuery, the open-source data integration engine Airbyte, the open-source full-stack BI platform Lightdash, and the open-source data transformation tool dbt.
Pragmatic ways of using Rust in your data project from Christopher Prohm
Writing efficient data pipelines in Python can be tricky. The standard recommendation is to use vectorized functions implemented in NumPy, pandas, or similar libraries. However, what do you do when the processing task does not fit these libraries? Using plain Python can result in poor performance, particularly when handling large datasets.
Actionable Machine Learning in the Browser with PyScript from Valero Maggio
PyScript brings the full PyData stack to the browser, opening up unprecedented use cases for interactive, data-intensive applications. In this scenario, the web browser becomes a ubiquitous computing platform, operating within a (nearly) zero-installation and serverless environment.
Hiring
We are hiring new talent for our AI/ML teams at Blue Yonder. If you are interested in one of the following positions—Senior Machine Learning Engineer, Senior Data Engineer, or Full-Stack Developer—just reach out to me.