PyData London - 95th Meetup

## Details

Venue: Riverbank House, 2 Swan Ln, London EC4R 3AD

Please note:

1. 🚨🚨🚨A valid photo ID is required by building security. 🚨🚨🚨
2. This event follows the NumFOCUS Code of Conduct, please familiarise yourself with it before the event.

If your RSVP status says "You're going" you will be able to get in. No need to show your RSVP confirmation when signing in.
If you can no longer make it, please unRSVP as soon as you know.
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Code of Conduct:
This event follows the NumFOCUS Code of Conduct. Please get in touch with the organisers with any questions or concerns regarding the Code of Conduct.
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As always, there'll be free food & drinks, generously provided by our host, Man Group.
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Main Talks

1. Death by RMSE: A Cautionary Tale of Metrics Gone Wild - John Sandall
You’ve trained the model. The eval metrics look great. But somehow, it doesn't change anything — the KPIs are static, the business impact isn’t there, and you’re left wondering: did we optimise for the wrong thing? In this talk we explore how the wrong model evaluation metric can quietly sabotage your machine learning team's impact, and hear my own first-hand experiences of where the choice between MAE, F1, RMSE and others wasn't academic but business critical. We'll cover the power of cohorting, backtesting to simulate past futures, how to align eval metrics to downstream company KPIs, and some stories of teams that got it all disastrously wrong. Whether you’re building models, leading teams, or just quietly suspicious that your ROC-AUC obsession might be misleading you… this talk is for you.

John is CEO of data science consultancy Coefficient, and CTO of stealth AI marketing startup Day30.

2. LLMs for Recommender Systems - Janu Verma
Large Language Models (LLMs) emerge as promising tools due to their world knowledge and reasoning capabilities, enabling nuanced understanding of user preferences and contextual patterns. In this talk, we will discuss how these models—designed primarily for language tasks— fare in recommendation tasks. I'll walk through my research on LLMs for recommendation problems. We will investigate pre-trained LLMs as zero and few-shot recommender systems as well as fine-tuning LLMs for recommendation tasks. We will discuss how to build a movie recommendation system by supervised fine-tuning a small-scale LLM on user interaction data.

⚡ Lightning Talks
1. What even is "\" in Python?. - Alex Caian

2. Beyond Conversation: Building AI Assistants That Take Action with Model Context Protocol - Amaboh (Ama) Achu

Logistics
Doors open at 6.30 pm (get there early as you have to sign-in via building security), talks start at 7 pm, drinks from 9 pm in the bar. We will have reduced capacity for this event but there will be plenty of people to discuss data science questions with!
Please unRSVP in good time if you realise you can't make it. We're limited by building security on the number of attendees, so please free up your place for your fellow community members!
Follow @pydatalondon (https://twitter.com/pydatalondon) for updates and early announcements.