PyData Rome, 9th Meeting, 21st January 2025

PyData Roma - 9th Meetup! 🎉

Let's pick up where we left off and continue our mission to make Rome a fantastic place for software engineering and data science.

The presentations for this event will be announced soon. You could be the next speaker for a future event. If you have a presentation, some interesting code, or an open problem you'd like to discuss with the community, compile the form and let us know! (Proposals can be in English or Italian, whatever makes you comfortable.)

Location: Via di Santa Prassede 24
Date: January 21st 2025

Schedule:

• 18:30 🚪 Door Opening
• 18.45 🏠 What is Immobiliare Labs - Andrea Giannantonio
• 19.00 🐍 What is Pydata Roma - Luigi Selmi
• 19:15 🎤 Notion2Pandas - A new python package to import Notion Database into Pandas framework and vice-versa by Andrea Rosati (Sviluppatore back-end GO @Screevo)
• 19.30 🎤 Beyond Deployment: Harnessing Open-Source Tools for Continuous Model Monitoring and Reliability by Lorenzo Massimiani (CV engineer @Immobiliare.it)
• 20.00 🤝 Socializing

Here is a short description of the two presentations:

1. Notion2Pandas - A new python package to import Notion Database into Pandas framework and vice-versa
A few months ago, I developed this Python package, notion2pandas, to simplify working with Notion databases using Notion's official API. I’d like not only to introduce the package but also to share my experience building a Python package from scratch and outline the roadmap for its future development.

2. Beyond Deployment: Harnessing Open-Source Tools for Continuous Model Monitoring and Reliability
Model monitoring is an essential yet frequently undervalued aspect of machine learning operations. Many organizations focus heavily on the development and deployment of models but often neglect the ongoing evaluation of their performance in production. This oversight can lead to issues such as model drift, reduced accuracy, and a lack of trust in automated decisions. Effective model monitoring ensures that predictions remain accurate and aligned with real-world conditions, fostering transparency and reliability.
This presentation introduces a streamlined process for evaluating machine learning models over time, utilizing open-source, Python-based tools such as Label Studio for manual annotations, Evidently for generating performance reports, and Streamlit for building intuitive report dashboards. The system automates key steps, including sampling and annotating data, generating detailed metrics, and visualizing results. Central to this workflow is a scheduler built with Apache Airflow, orchestrating tasks like data import, annotation validation, and report generation.
Attendees will gain insights into the importance of model monitoring, discover a scalable framework for maintaining model accuracy, and learn how open-source tools can facilitate transparency and accountability in machine learning systems.

Sign Up!
Space is limited, so RSVP today to secure your spot!

Please note:
Remember to RSVP using your full name for security reasons and bring a valid ID to show at the entrance.
If you can't attend, please let us know at least 2 days in advance so you can free spots for people on the waiting list. We look forward to seeing you there! 🙌