Adrin Jalali

The EU AI Act is already partly in effect which prohibits certain AI systems. After going through the basics, we cover some of the less talked about aspects of the Act, introducing entities involved in its implementation and how many high risk government and law enforcement use cases are excluded!

The EU AI Act, partly in effect, is a groundbreaking regulatory framework governing AI systems based on perceived risk. This talk covers the basics and explores lesser-discussed aspects, including implementation entities, the private sector's role, and exemptions for high-risk government and law...

Let's live code and develop some scikit-learn compatible {meta, simple}-estimators together. This talk covers recent API developments in scikit-learn, and gives you the tools to handle scikit-learn API requirements. We also show how to be compatible across scikit-learn versions.

This talk teaches you how to use some python black magic to add methods to child classes of a parent class, based on certain properties of the child class. Introspection, descriptors, and `__init_subclass__` are going to be our friends.

Passing metadata such as `sample_weight` and `groups` through a scikit-learn `cross_validate`, `GridSearchCV`, or a `Pipeline` to the right estimators, scorers, and CV splitters has been either cumbersome, hacky, or impossible. The new metadata routing mechanism in scikit-learn enables you to...

Loading pickle files can run arbitrary code on your system. This talk presents how this can be exploited, and how `skops` is tackling the issue for scikit-learn/statistical ML models.

In certain areas of the industry open source has become mainstream, whether it be a small part of a product, a “community edition of a product”, or creating a whole business around an open source product.

Machine learning is almost always used in systems which automate or semi-automate decision making processes. These decisions are used in recommender systems, fraud detection, healthcare recommendation systems, etc.