Name: PyData Milano: Kickoff Event for the New Season!
Start: 2024-11-19T19:00:00+01:00
End: 2024-11-19T21:00:00+01:00
Location: Spotify

**✨ 🚀 PyData Milano New Season! 🐍 ✨**

**Date**: November 19, 2024
📍 **Venue**: Spotify, Via Joe Colombo, 4, 20124 Milano MI
**Agenda**:

* 👋 **18:30** \- Opening Gates
* 🎤 **19:00** Short Meetup presentation
* ⭐️ **19:10** \- Talk 1: From stringly typed to strongly typed: Insights from re\-designing a library
* **Speaker:** Janos Gabler, Senior AI Researcher at appliedAI and **creator of optimagic**
* ⭐️ **19:55** \- Talk 2: Foundational Models for Time Series Forecasting: Are We There Yet?
* **Speaker:** Luca Baggi, ML Expert @xtream
* 🍻**21:00** \- Cena e networking

Join us for the first event of the new PyData season! We have two exciting talks lined up that you won’t want to miss.

**From stringly typed to strongly typed: Insights from re-designing a library to get the most out of static typing**
**Speaker**: Janos Gabler, Senior AI Researcher at appliedAI and **creator of optimagic**
**Description**:
Many scientific Python packages are “stringly typed,” using strings to select algorithms or methods and dictionaries for configuration. While this approach is easy for beginners and convenient for authors, it misses out on the benefits of static typing, such as error detection before runtime and autocomplete. In this talk, Janos Gabler will share insights from redesigning the optimagic library with static typing in mind. Learn how to achieve better static analysis, autocomplete, and fewer runtime errors without compromising simplicity. These insights apply to a wide range of scientific Python packages.

**Foundational Models for Time Series Forecasting: are we there yet?**
**Speaker**: Luca Baggi, ML Expert @xtream
**Description**:
Transformers are revolutionizing fields like NLP, Computer Vision, sound generation, and even protein-folding. But what about forecasting? This talk will explore how transformer-based models like Amazon Chronos and Salesforce’s Moirai are being adapted for time series forecasting. Understand the datasets used, the evaluation methods, and whether these models are a good fit for your use-case. This pragmatic talk is perfect for Generative AI and ML practitioners eager to understand the adaptation of transformer-based architectures to forecasting problems.

**RSVP**: Now on Meetup to secure your spot!
✋ **Please use your actual full name, nick names are not allowed.**

**Note**: If you know you won’t be able to attend, **please cancel your RSVP to free up your spot** for someone else interested in participating.

Paolo Finardi

Emanuele Roppo

Giulio Mazzanti

Cesare Placanica

Luca Baggi

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