Valerio Maggio
Valerio Maggio is a Researcher, a Data scientist Advocate at Anaconda, and a casual "Magic: The Gathering" wizard. He is well versed in open science and research software, supporting the adoption of best software development practice (e.g. Code Review) in Data Science. Valerio is also an open-source contributor, and an active member of the Python community. Over the last twelve years he has contributed and volunteered to the organization of many international conferences and community meetups like PyCon Italy, PyData, EuroPython, and EuroSciPy. All his talks, workshop materials and random ramblings are publicly available on his Speaker Deck and GitHub profiles.
Sessions
Privacy guarantee is the most crucial requirement when it comes to analyse sensitive data. However, data anonymisation techniques alone do not always provide complete privacy protection; moreover Machine Learning models could also be exploited to leak sensitive data when attacked, and no counter-measure is applied. Privacy-preserving machine learning (PPML) methods hold the promise to overcome all these issues, allowing to train machine learning models with full privacy guarantees. In this tutorial we will explore several methods for privacy-preserving data analysis, and how these techniques can be used to safely train ML models without actually seeing the data.
The Python Data Model is __magic__ (like, literally!) as it provides everything you need to magic (as in intuitive, reusable, and easy-to-maintain) data abstractions. And what a better use case than processing actual Magic™ data ? This time I mean Magic: The Gathering™ (M:TG) the card game!
In this talk, we will deep dive into the the power of __dunders__ and the Python Data Model to create flexible and pythonic data abstractions to process M:TG data for complex machine learning pipelines.