asyncio is the de-facto standard for asynchronous programming in Python and enables concurrent operations without using threads or processes.

In this talk, we will delve into the technical details of asyncio and show how it can be used to improve the performance of Python applications. We will start by discussing the difference between threading, multiprocessing and async programming. Then, we will introduce the basic building blocks of asyncio: Event loops and Coroutines. We will dive deep into the way Coroutines work, discussing their origins and how they are linked to Generators.

Next, we will look at Tasks, which are a higher-level abstraction built on top of Coroutines. Tasks make it easy to schedule and manage the execution of Coroutines. We will cover how to create and manage Tasks and how they can be used to write concurrent code.

Finally, we will also cover some more advanced topics such as Async Loops and Context Managers, and how to handle errors and cancellations in asyncio.

Whether you are new to asyncio or have experience with it, this talk will provide valuable insights and tips for leveraging its full potential. By the end of this talk, you will have a better understanding of how asyncio works, and how to use it to create efficient, high-performing Python applications.

XGBoost is considered a state-of-the-art model for regression, classification, and learning-to-rank problems on tabular data. Unfortunately, tree-based ensemble models are notoriously difficult to explain, limiting their application in critical fields. Techniques like SHapley Additive exPlanations (SHAP) and Explainable Boosting Machine (EBM) have become common methods for assessing how much each feature contributes to the model prediction.

This talk will introduce SHAP and EBM, explaining the theory behind their mechanisms in an accessible way and discussing the pros and cons of both techniques. We will also comment on Python snippets where SHAP and EBM are used to explain a gradient boosting model.

Attendees will walk away with an understanding of how SHAP and EBM work, the limitations and merits of both techniques, and a tutorial on how to use these methods in Python, courtesy of the shap and interpret-ml packages.

Talk outline:

• A brief reminder about gradient boosting and XGBoost (5 mins)
• The challenge of explainability (5 mins)
• EBM: theory and applications (10 mins)
• SHAP: theory and applications (10 mins)